{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:28.537558Z",
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sys.version_info(major=3, minor=10, micro=14, releaselevel='final', serial=0)\n",
      "matplotlib 3.10.0\n",
      "numpy 1.26.4\n",
      "pandas 2.2.3\n",
      "sklearn 1.6.0\n",
      "torch 2.5.1+cu124\n",
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "from tqdm.auto import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, torch:\n",
    "    print(module.__name__, module.__version__)\n",
    "\n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)\n",
    "\n",
    "seed = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c29f1d6fe09a9955",
   "metadata": {},
   "source": [
    "# 数据加载\n",
    "\n",
    "- 采用WMT16的德语和英语平行语料库，数据集主页：[WMT16](https://www.statmt.org/wmt16/multimodal-task.html#task1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d047d1eed0336fbe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:28.942222Z",
     "start_time": "2025-02-06T14:04:28.938639Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T12:20:44.041387Z",
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     "iopub.status.idle": "2025-02-07T12:20:44.044252Z",
     "shell.execute_reply": "2025-02-07T12:20:44.043699Z",
     "shell.execute_reply.started": "2025-02-07T12:20:44.041363Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # 调用脚本文件\n",
    "# !pip install sacremoses\n",
    "# !sh data_multi30k.sh wmt16 wmt16_cut de en"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a930b5668e422750",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c0f0f4781f5e042f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.020273Z",
     "start_time": "2025-02-06T14:04:29.011246Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T12:20:44.045280Z",
     "iopub.status.busy": "2025-02-07T12:20:44.045002Z",
     "iopub.status.idle": "2025-02-07T12:20:44.053614Z",
     "shell.execute_reply": "2025-02-07T12:20:44.052909Z",
     "shell.execute_reply.started": "2025-02-07T12:20:44.045260Z"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "\n",
    "class LangPairDataset(Dataset):\n",
    "    def __init__(self, mode=\"train\", max_length=128,\n",
    "                 overwrite_cache=False, data_dir=\"wmt16\"):\n",
    "        self.data_dir = Path(data_dir)\n",
    "        cache_path = self.data_dir / \".cache\" / f\"de2en_{mode}_{max_length}.npy\"\n",
    "\n",
    "        if overwrite_cache or not cache_path.exists():\n",
    "            # parents=True：如果父目录不存在，会自动创建所有必要的父目录。\n",
    "            # exist_ok=True：如果目录已经存在，不会抛出错误。\n",
    "            cache_path.parent.mkdir(parents=True, exist_ok=True)\n",
    "            # 读取源语言文件所有行\n",
    "            with open(self.data_dir / f\"{mode}_src.bpe\", \"r\", encoding=\"utf8\") as f:\n",
    "                self.src = f.readlines()\n",
    "            # 读取目标语言文件所有行\n",
    "            with open(self.data_dir / f\"{mode}_trg.bpe\", \"r\", encoding=\"utf8\") as f:\n",
    "                self.trg = f.readlines()\n",
    "\n",
    "            filtered_src = []\n",
    "            filtered_trg = []\n",
    "            # max length filter,超出最大长度的句子舍弃\n",
    "            for src, trg in zip(self.src, self.trg):\n",
    "                # 过滤长度超过最大长度的句子\n",
    "                if len(src) <= max_length and len(trg) <= max_length:\n",
    "                    filtered_src.append(src.strip())  # 去掉句子前后的空格\n",
    "                    filtered_trg.append(trg.strip())\n",
    "            filtered_src = np.array(filtered_src)\n",
    "            filtered_trg = np.array(filtered_trg)\n",
    "            #allow_pickle=True允许保存对象数组，将过滤后的数据保存为 NumPy 数组，存储在缓存文件中\n",
    "            np.save(\n",
    "                cache_path,\n",
    "                {\"src\": filtered_src, \"trg\": filtered_trg},\n",
    "                allow_pickle=True\n",
    "            )\n",
    "            print(f\"save cache to {cache_path}\")\n",
    "\n",
    "        else:\n",
    "            # allow_pickle=True允许保存对象数组\n",
    "            cache_dict = np.load(cache_path, allow_pickle=True).item()\n",
    "            print(f\"load {mode} dataset from {cache_path}\")\n",
    "            filtered_src = cache_dict[\"src\"]\n",
    "            filtered_trg = cache_dict[\"trg\"]\n",
    "\n",
    "        self.src = filtered_src\n",
    "        self.trg = filtered_trg\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        return self.src[index], self.trg[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.src)\n",
    "\n",
    "# train_ds = LangPairDataset(\"train\")\n",
    "# val_ds = LangPairDataset(\"val\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a2a0f23be5871b84",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.097548Z",
     "start_time": "2025-02-06T14:04:29.094294Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T12:20:44.054503Z",
     "iopub.status.busy": "2025-02-07T12:20:44.054279Z",
     "iopub.status.idle": "2025-02-07T12:20:44.057051Z",
     "shell.execute_reply": "2025-02-07T12:20:44.056516Z",
     "shell.execute_reply.started": "2025-02-07T12:20:44.054485Z"
    }
   },
   "outputs": [],
   "source": [
    "# print(len(train_ds))\n",
    "# print(len(val_ds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eaf27846b953da4",
   "metadata": {},
   "source": [
    "## Tokenizer\n",
    "\n",
    "这里有两种处理方式，分别对应着 encoder 和 decoder 的 word embedding 是否共享，这里实现共享的方案"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "df6173e2f201c301",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.169362Z",
     "start_time": "2025-02-06T14:04:29.138854Z"
    },
    "execution": {
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     "iopub.status.idle": "2025-02-07T12:20:44.082966Z",
     "shell.execute_reply": "2025-02-07T12:20:44.082479Z",
     "shell.execute_reply.started": "2025-02-07T12:20:44.057763Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 18107/18107 [00:00<00:00, 1120448.83it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vocab_size: 18111\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "word2idx = {\n",
    "    \"[PAD]\": 0,  # 填充 token\n",
    "    \"[BOS]\": 1,  # begin of sentence\n",
    "    \"[UNK]\": 2,  # 未知 token\n",
    "    \"[EOS]\": 3,  # end of sentence\n",
    "}\n",
    "\n",
    "idx2word = {value: key for key, value in word2idx.items()}\n",
    "index = len(idx2word)\n",
    "threshold = 1  # 出现次数低于此的token舍弃\n",
    "\n",
    "with open(\"wmt16/vocab\", \"r\", encoding=\"utf8\") as fd:\n",
    "    for line in tqdm(fd.readlines()):\n",
    "        token, counts = line.strip().split()\n",
    "        if int(counts) >= threshold:\n",
    "            word2idx[token] = index\n",
    "            idx2word[index] = token\n",
    "            index += 1\n",
    "\n",
    "vocab_size = len(word2idx)\n",
    "print(\"vocab_size: {}\".format(vocab_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "461056213fba3ed4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.226412Z",
     "start_time": "2025-02-06T14:04:29.217164Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T12:20:44.083878Z",
     "iopub.status.busy": "2025-02-07T12:20:44.083608Z",
     "iopub.status.idle": "2025-02-07T12:20:44.092587Z",
     "shell.execute_reply": "2025-02-07T12:20:44.091979Z",
     "shell.execute_reply.started": "2025-02-07T12:20:44.083861Z"
    }
   },
   "outputs": [],
   "source": [
    "class Tokenizer:\n",
    "    def __init__(self, word2idx, idx2word, max_length=128,\n",
    "                 pad_idx=0, bos_idx=1, eos_idx=3, unk_idx=2):\n",
    "        self.word2idx = word2idx\n",
    "        self.idx2word = idx2word\n",
    "        self.max_length = max_length\n",
    "        self.pad_idx = pad_idx\n",
    "        self.bos_idx = bos_idx\n",
    "        self.eos_idx = eos_idx\n",
    "        self.unk_idx = unk_idx\n",
    "\n",
    "    def encode(self, text_list, padding_first=False, add_bos=True, add_eos=True,\n",
    "               return_mask=False):\n",
    "        # 如果padding_first == True，则padding加载前面，否则加载后面\n",
    "        # return_mask: 是否返回mask(掩码），mask用于指示哪些是padding的，哪些是真实的token \n",
    "        # 若启动bos_eos，则在句首和句尾分别添加bos和eos，则 add_bos=True, add_eos=True即都为1\n",
    "        max_length = min(self.max_length, add_bos + add_eos + max([len(text) for text in text_list]))\n",
    "        indices_list = []\n",
    "        for text in text_list:\n",
    "            # 如果词表中没有这个词，就用unk_idx代替，indices是一个list,里面是每个词的index,也就是一个样本的index\n",
    "            indices = [self.word2idx.get(word, self.unk_idx) for word in text[:max_length - add_eos - add_bos]]\n",
    "            if add_bos:\n",
    "                indices = [self.bos_idx] + indices\n",
    "            if add_eos:\n",
    "                indices = indices + [self.eos_idx]\n",
    "            if padding_first:  # padding加载前面，超参可以调\n",
    "                indices = [self.pad_idx] * (max_length - len(indices)) + indices\n",
    "            else:  # padding加载后面\n",
    "                indices = indices + [self.pad_idx] * (max_length - len(indices))\n",
    "            indices_list.append(indices)\n",
    "        input_ids = torch.tensor(indices_list)  # 转换为tensor\n",
    "        # mask是一个和input_ids一样大小的tensor，0代表token，1代表padding，mask用于去除padding的影响\n",
    "        masks = (input_ids == self.pad_idx).to(dtype=torch.float64)\n",
    "        return input_ids if not return_mask else (input_ids, masks)\n",
    "\n",
    "    def decode(self, indices_list, remove_bos=True, remove_eos=True, remove_pad=True, split=False):\n",
    "        text_list = []\n",
    "        for indices in indices_list:\n",
    "            text = []\n",
    "            for index in indices:\n",
    "                # 如果词表中没有这个词，就用unk_idx代替\n",
    "                word = self.idx2word.get(index, \"[UNK]\")\n",
    "                if remove_bos and word == \"[BOS]\":\n",
    "                    continue\n",
    "                if remove_eos and word == \"[EOS]\":  # 如果到达eos，就结束\n",
    "                    break\n",
    "                if remove_pad and word == \"[PAD]\":  # 如果到达pad，就结束\n",
    "                    break\n",
    "                text.append(word)  # 单词添加到列表中\n",
    "            # 把列表中的单词拼接，变为一个句子\n",
    "            text_list.append(\" \".join(text) if not split else text)\n",
    "        return text_list\n",
    "\n",
    "\n",
    "tokenizer = Tokenizer(word2idx=word2idx, idx2word=idx2word)\n",
    "\n",
    "# tokenizer.encode([[\"hello\"], [\"hello\", \"world\"]], add_bos=True, add_eos=False)\n",
    "# raw_text = [\"hello world\".split(), \"tokenize text datas with batch\".split(), \"this is a test\".split()]\n",
    "# indices = tokenizer.encode(raw_text, padding_first=False, add_bos=True, add_eos=True)\n",
    "# decode_text = tokenizer.decode(indices.tolist(), remove_bos=False, remove_eos=False, remove_pad=False)\n",
    "# print(\"raw text\")\n",
    "# for raw in raw_text:\n",
    "#     print(raw)\n",
    "# print(\"indices\")\n",
    "# for index in indices:\n",
    "#     print(index)\n",
    "# print(\"decode text\")\n",
    "# for decode in decode_text:\n",
    "#     print(decode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "628dded826e86608",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.254457Z",
     "start_time": "2025-02-06T14:04:29.250423Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T12:20:44.094611Z",
     "iopub.status.busy": "2025-02-07T12:20:44.094317Z",
     "iopub.status.idle": "2025-02-07T12:20:44.097034Z",
     "shell.execute_reply": "2025-02-07T12:20:44.096558Z",
     "shell.execute_reply.started": "2025-02-07T12:20:44.094589Z"
    }
   },
   "outputs": [],
   "source": [
    "# for i,j in train_ds:\n",
    "#     print(len(i))\n",
    "#     print(len(j))\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d43df9ce1d4860bb",
   "metadata": {},
   "source": [
    "## Transformer Batch Sampler\n",
    "\n",
    "> Sentence pairs were batched together by approximate sequence length. Each training batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000 target tokens\n",
    "句子按照序列长度差不多的分到一个批次。 每个训练批次包含一组句子对，其中包含大约 25000 个源标记和 25000 个目标标记"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7c072c0e714bd8ea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.298858Z",
     "start_time": "2025-02-06T14:04:29.288864Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T12:20:44.097853Z",
     "iopub.status.busy": "2025-02-07T12:20:44.097681Z",
     "iopub.status.idle": "2025-02-07T12:20:44.103905Z",
     "shell.execute_reply": "2025-02-07T12:20:44.103349Z",
     "shell.execute_reply.started": "2025-02-07T12:20:44.097836Z"
    }
   },
   "outputs": [],
   "source": [
    "# 下面的info对象\n",
    "class SampleInfo:\n",
    "    def __init__(self, i, lens):\n",
    "        \"\"\"\n",
    "        记录文本对的序号和长度信息\n",
    "        输入：\n",
    "            - i (int): 文本对的序号。\n",
    "            - lens (list): 文本对源语言和目标语言的长度\n",
    "        \"\"\"\n",
    "        self.i = i\n",
    "        # 加一是考虑填补在文本前后的特殊词元，lens[0]和lens[1]分别表示源语言和目标语言的长度\n",
    "        self.max_len = max(lens[0], lens[1]) + 1\n",
    "        self.src_len = lens[0] + 1  # 加一是考虑填补在文本前后的特殊词元\n",
    "        self.trg_len = lens[1] + 1  # 加一是考虑填补在文本前后的特殊词元\n",
    "\n",
    "\n",
    "# 一个批量生成器，根据词元数目的限制来控制批量的大小。\n",
    "# 它会根据传入的样本信息，在不超过设定大小的情况下，逐步构建批量。\n",
    "class TokenBatchCreator:\n",
    "    def __init__(self, batch_size):\n",
    "        \"\"\"\n",
    "        参数:\n",
    "        batch_size (int): 用于限制批量的大小。\n",
    "        功能:\n",
    "        初始化了一个空的批量列表 _batch。\n",
    "        设定了初始的最大长度为 -1。\n",
    "        存储了传入的 batch_size。\n",
    "        \"\"\"\n",
    "        self._batch = []  # 这个就是之前的batch_size，就是一个batch内有多少个样本\n",
    "        self.max_len = -1\n",
    "        self.batch_size = batch_size  # 限制批量的大小,假设是4096\n",
    "\n",
    "    def append(self, info: SampleInfo):\n",
    "        \"\"\"\n",
    "        参数:\n",
    "        info (SampleInfo): 文本对的信息。\n",
    "        功能:\n",
    "        接收一个 SampleInfo 对象，并根据其最大长度信息更新当前批量的最大长度。\n",
    "        如果将新的样本加入批量后超过了批量大小限制，它会返回已有的批量并将新的样本加入新的批量。\n",
    "        否则，它会更新最大长度并将样本添加到当前批量中。\n",
    "        \"\"\"\n",
    "        # 更新当前批量的最大长度\n",
    "        cur_len = info.max_len  # 当前样本的长度\n",
    "        max_len = max(self.max_len, cur_len)  # 每来一个样本，更新当前批次的最大长度\n",
    "\n",
    "        # 如果新的样本加入批量后超过大小限制，则将已有的批量返回，新的样本加入新的批量\n",
    "        # 同一批样本计算时，短的样本向最长的样本对齐，所以用max_len来计算\n",
    "        if max_len * (len(self._batch) + 1) > self.batch_size:\n",
    "            # result_batch保存原有的_batch，_batch清空\n",
    "            self._batch, result_batch = [], self._batch\n",
    "            self._batch.append(info)  #箱子里的第一条样本，放入\n",
    "            # 因为是当前batch的第一个样本，所以它的长度就是当前长度\n",
    "            self.max_len = cur_len\n",
    "            return result_batch\n",
    "        else:\n",
    "            self.max_len = max_len\n",
    "            self._batch.append(info)\n",
    "            return None\n",
    "\n",
    "    # 将类的方法转换为属性，从而实现对类属性的访问、修改和删除的控制\n",
    "    @property\n",
    "    def batch(self):\n",
    "        return self._batch"
   ]
  },
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   "id": "c1dd026c49a90114",
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   "source": [
    "from torch.utils.data import BatchSampler\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "class TransformerBatchSampler(BatchSampler):\n",
    "    def __init__(self, dataset, batch_size, shuffle_batch=False,\n",
    "                 clip_last_batch=False, seed=0):\n",
    "        \"\"\"\n",
    "        批量采样器\n",
    "        输入:\n",
    "            - dataset: 数据集\n",
    "            - batch_size: 批量大小\n",
    "            - shuffle_batch: 是否对生成的批量进行洗牌\n",
    "            - clip_last_batch: 是否裁剪最后剩下的数据\n",
    "            - seed: 随机数种子\n",
    "        \"\"\"\n",
    "        self._dataset = dataset\n",
    "        self._batch_size = batch_size\n",
    "        self._shuffle_batch = shuffle_batch\n",
    "        self._clip_last_batch = clip_last_batch\n",
    "        self._seed = seed\n",
    "        self._random = np.random\n",
    "        self._random.seed(seed)\n",
    "\n",
    "        self._sample_infos = []\n",
    "        # 根据数据集中的每个样本，创建了对应的 SampleInfo 对象，包含了样本的索引和长度信息\n",
    "        for i, data in enumerate(self._dataset):\n",
    "            lens = [len(data[0]), len(data[1])]  #输入和输出的长度计算放到lens中\n",
    "            self._sample_infos.append(SampleInfo(i, lens))\n",
    "\n",
    "    def __iter__(self):\n",
    "        \"\"\"\n",
    "        对数据集中的样本进行排序，排序规则是先按源语言长度排序，如果相同则按目标语言长度排序。\n",
    "        使用 TokenBatchCreator 逐步组装批量数据，当满足批量大小时返回一个批量的样本信息。\n",
    "        如果不裁剪最后一个批次的数据且存在剩余样本，则将这些样本组成最后一个批次。\n",
    "        如果需要对批量进行洗牌，则对批次进行洗牌操作。\n",
    "        通过迭代器，抛出每个批量的样本在数据集中的索引。\n",
    "        \"\"\"\n",
    "        # 排序，如果源语言长度相同则按照目标语言的长度排列\n",
    "        infos = sorted(self._sample_infos,\n",
    "                       key=lambda x: (x.src_len, x.trg_len))\n",
    "        # 组装批量，所有的batch都放入batch_infos\n",
    "        batch_infos = []\n",
    "        # 批量生成器\n",
    "        batch_creator = TokenBatchCreator(self._batch_size)\n",
    "        for info in infos:\n",
    "            batch = batch_creator.append(info)\n",
    "            # 存够一个batch的样本信息后，会把这个batch返回，否则返回为None\n",
    "            if batch is not None:\n",
    "                batch_infos.append(batch)\n",
    "\n",
    "        # 是否抛弃最后批量的文本对\n",
    "        # 如果最后一个batch的样本数目不足一个batch，则将其剩余的样本作为最后一个batch\n",
    "        if not self._clip_last_batch and len(batch_creator.batch) != 0:\n",
    "            batch_infos.append(batch_creator.batch)  # 最后一个batch\n",
    "\n",
    "        # 打乱batch\n",
    "        # 这里的shuffle是对batch_infos进行shuffle，而不是对batch_infos中的样本进行shuffle\n",
    "        if self._shuffle_batch:\n",
    "            self._random.shuffle(batch_infos)\n",
    "\n",
    "        self.batch_number = len(batch_infos)\n",
    "\n",
    "        # 抛出一个批量的文本对在数据集中的序号\n",
    "        for batch in batch_infos:\n",
    "            # info.i 是文本对的索引\n",
    "            batch_indices = [info.i for info in batch]\n",
    "            yield batch_indices\n",
    "\n",
    "    def __len__(self):\n",
    "        \"\"\"\n",
    "        返回批量的数量\n",
    "        \"\"\"\n",
    "        if hasattr(self, \"batch_number\"):\n",
    "            return self.batch_number\n",
    "        # 计算批量的数量,没有用到下面的情况，不用看\n",
    "        batch_number = (len(self._dataset) +\n",
    "                        self._batch_size) // self._batch_size\n",
    "        return batch_number\n"
   ]
  },
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   "cell_type": "code",
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   "id": "71ae51fb9433db98",
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   "outputs": [],
   "source": [
    "# sampler = TransformerBatchSampler(train_ds, batch_size=4096, shuffle_batch=True)\n",
    "# \n",
    "# #为什么这里每个批量的样本对数目不一样呢？长度*batch_number>4096的时候，就会返回上一个batch，然后新的样本加入新的batch,具体要看TokenBatchCreator的40行\n",
    "# for idx, batch in enumerate(sampler):\n",
    "#     print(\"第{}批量的数据中含有文本对是：{}，数量为：{}\".format(idx, batch, len(batch)))\n",
    "#     if idx >= 3:\n",
    "#         break\n",
    "# \n",
    "# print(\"-\"*50)\n",
    "# print(len(sampler))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98b1add788035b2a",
   "metadata": {},
   "source": [
    "## DataLoader"
   ]
  },
  {
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   "id": "24e99727ef49605",
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   "source": [
    "def collate_fn(batch, tokenizer):\n",
    "    # 取batch内第0列进行分词，赋给src_words\n",
    "    src_words = [pair[0].split() for pair in batch]\n",
    "    # 取batch内第1列进行分词，赋给trg_words\n",
    "    trg_words = [pair[1].split() for pair in batch]\n",
    "\n",
    "    # paddings 在前在后影响不大，但在后好理解，所以padding_first=False\n",
    "    # [BOS] src [EOS] [PAD] \n",
    "    encoder_inputs, encoder_inputs_mask = tokenizer.encode(\n",
    "        src_words, padding_first=False, add_bos=True, add_eos=True, return_mask=True\n",
    "    )\n",
    "\n",
    "    # 目标语言 [BOS] trg [PAD]\n",
    "    decoder_inputs = tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=True, add_eos=False, return_mask=False\n",
    "    )\n",
    "\n",
    "    # 用于后续计算loss\n",
    "    # trg [EOS] [PAD]\n",
    "    decoder_labels, decoder_labels_mask = tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=False, add_eos=True, return_mask=True\n",
    "    )\n",
    "\n",
    "    return {\n",
    "        \"encoder_inputs\": encoder_inputs.to(device),\n",
    "        \"encoder_inputs_mask\": encoder_inputs_mask.to(device),\n",
    "        \"decoder_inputs\": decoder_inputs.to(device),\n",
    "        \"decoder_labels\": decoder_labels.to(device),\n",
    "        \"decoder_labels_mask\": decoder_labels_mask.to(device),\n",
    "    }  # 当返回的数据较多时，用dict返回比较合理\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f53f13a987ea9201",
   "metadata": {
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   "source": [
    "from functools import partial  # 固定collate_fct的tokenizer参数\n",
    "\n",
    "# # 可以调大batch_size,来看最终的bleu，如果GPU内存不够，可以减小batch_size\n",
    "# sampler = TransformerBatchSampler(train_ds, batch_size=256, shuffle_batch=True)\n",
    "# \n",
    "# # batch_sampler 是一个自定义的批次采样器，用于控制如何从数据集中采样批次。与 batch_size 和 shuffle 不同，batch_sampler 可以更灵活地定义批次的组织方式（如按长度分组）。\n",
    "# # partial(collate_fn, tokenizer=tokenizer) 使用 functools.partial 将 tokenizer 参数固定到 collate_fn 中，生成一个新的函数。\n",
    "# sample_dl = DataLoader(train_ds, batch_sampler=sampler, collate_fn=partial(collate_fn, tokenizer=tokenizer))\n",
    "# \n",
    "# for batch in sample_dl:  #外层是拿每个batch\n",
    "#     for key, value in batch.items():  #内层是拿每个batch里面是一个字典\n",
    "#         print(key)\n",
    "#         print(\"-\" * 50)\n",
    "#         print(value)\n",
    "#         print(\"-\" * 50)\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa4932b257d82059",
   "metadata": {},
   "source": [
    "# 定义模型\n",
    "\n",
    "- Transformer模型由Embedding、Transformer-Block组成\n",
    "- Embedding包括：\n",
    "    - WordEmbedding\n",
    "    - PositionEmbedding\n",
    "- Transformer-Block包括：\n",
    "    - Self-Attention\n",
    "    - Cross-Attention\n",
    "    - MLP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d2988138abac64d",
   "metadata": {},
   "source": [
    "## Embedding"
   ]
  },
  {
   "cell_type": "code",
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   "id": "847a70137968848a",
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    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class TransformerEmbedding(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.vocab_size = config[\"vocab_size\"]\n",
    "        self.hidden_size = config[\"d_model\"]  # 词向量维度\n",
    "        self.pad_idx = config[\"pad_idx\"]\n",
    "        dropout_rate = config[\"dropout\"]\n",
    "        self.max_length = config[\"max_length\"]\n",
    "\n",
    "        # layers,设置padding_idx可以让pad的词向量全为0\n",
    "        self.word_embedding = nn.Embedding(\n",
    "            self.vocab_size, self.hidden_size, padding_idx=self.pad_idx\n",
    "        )\n",
    "        self.position_embedding = nn.Embedding(\n",
    "            self.max_length, self.hidden_size,\n",
    "            # 位置编码，权重通过get_positional_encoding函数计算得到\n",
    "            _weight=self.get_position_encoding(self.max_length, self.hidden_size)\n",
    "        )\n",
    "\n",
    "        self.position_embedding.weight.requires_grad_(False)  # 不更新位置编码的权重\n",
    "        self.dropout = nn.Dropout(dropout_rate)  # 随机失活层\n",
    "\n",
    "    def get_word_embedding_weight(self):\n",
    "        return self.word_embedding.weight\n",
    "\n",
    "    # 计算位置信息\n",
    "    # 用于定义类方法（class method）。\n",
    "    # 类方法是绑定到类而不是实例的方法，可以通过类本身或类的实例调用。\n",
    "    @classmethod\n",
    "    def get_position_encoding(self, max_length, hidden_size):\n",
    "        # max_length是最大长度，hidden_size是embedding维度相等\n",
    "        pe = torch.zeros(max_length, hidden_size)  # 初始化位置编码\n",
    "        # .unsqueeze(1) 是将这个一维张量转换为二维张量，\n",
    "        # 即将其形状从 (max_length,) 变为 (max_length, 1)。\n",
    "        # 这个操作在张量的维度上增加了一个维度，使其从一维变为二维，第二维的大小为 1。\n",
    "        position = torch.arange(0, max_length).unsqueeze(1)  # 位置信息,从0到max_length-1\n",
    "        # 计算位置编码的权重,为了性能考量（是数学上的对数函数分解），这里用了log函数\n",
    "        # torch.arange(0, hidden_size, 2) 在 PyTorch 中用于创建一个一维张量，包含从 0 开始到（但不包括）hidden_size 的数值，步长为 2\n",
    "        div_term = torch.exp(\n",
    "            torch.arange(0, hidden_size, 2) *\n",
    "            -(torch.log(torch.Tensor([10000.0])) / hidden_size)\n",
    "        )\n",
    "        pe[:, 0::2] = torch.sin(position * div_term)  # 偶数位置\n",
    "        pe[:, 1::2] = torch.cos(position * div_term)  # 奇数位置\n",
    "        return pe\n",
    "\n",
    "    def forward(self, input_ids):\n",
    "        # input_ids: [batch_size,seq_len]\n",
    "        seq_len = input_ids.shape[1]\n",
    "        assert (\n",
    "                seq_len <= self.max_length), f\"input sequence length should no more than {self.max_length} but got {seq_len}\"\n",
    "\n",
    "        position_ids = torch.arange(seq_len, dtype=torch.long, device=input_ids.device)  # 位置信息\n",
    "        # unsqueeze(0): 在第一维添加一个维度，使张量的形状从 [seq_len] 变为 [1, seq_len]\n",
    "        # expand_as(input_ids): 扩展张量的形状，使其与 input_ids 张量的形状相同\n",
    "        position_ids = position_ids.unsqueeze(0).expand_as(input_ids)\n",
    "        # position_ids: [batch_size,seq_len]\n",
    "\n",
    "        # print(input_ids.shape) #为了调试\n",
    "        # print(position_ids.shape) #为了调试\n",
    "        # print(position_ids) #为了调试\n",
    "\n",
    "        # embedding层\n",
    "        word_embeds = self.word_embedding(input_ids)  # 词嵌入\n",
    "        # word_embeds: [batch_size,seq_len,hidden_size]\n",
    "        # position_ids: [batch_size,seq_len]\n",
    "        pos_embeds = self.position_embedding(position_ids)  # 位置嵌入 # ？\n",
    "        # pos_embeds: [batch_size,seq_len,hidden_size]   \n",
    "        embeds = word_embeds + pos_embeds  # 相加得到嵌入向量\n",
    "        # embeds: [batch_size,seq_len,hidden_size]\n",
    "        embeds = self.dropout(embeds)  # 随机失活\n",
    "        return embeds\n",
    "\n",
    "\n",
    "# #随机input，调用TransformerEmbedding\n",
    "# config={\n",
    "#     \"vocab_size\": 100,\n",
    "#     \"d_model\": 128,\n",
    "#     \"pad_idx\": 0,\n",
    "#     \"max_length\": 64,\n",
    "#     \"dropout\": 0.1,\n",
    "# }\n",
    "# input_ids = torch.randint(0, 100, (2, 50))\n",
    "# embeds = TransformerEmbedding(config)(input_ids)\n",
    "# embeds.shape\n",
    "\n",
    "# 绘制位置编码\n",
    "def plot_position_embedding(position_embedding):\n",
    "    plt.pcolormesh(position_embedding)  # 绘制位置编码矩阵\n",
    "    plt.xlabel('Depth')\n",
    "    plt.ylabel('Position')\n",
    "    plt.colorbar()  # 颜色条，-1到1的颜色范围\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "position_embedding = TransformerEmbedding.get_position_encoding(64, 128)\n",
    "plot_position_embedding(position_embedding)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39630e37b910bd7f",
   "metadata": {},
   "source": [
    "## Transformer Block"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72e7edad4645f763",
   "metadata": {},
   "source": [
    "### scaled-dot-product-attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7bd08ede5d628f45",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.834753Z",
     "start_time": "2025-02-06T14:04:29.821751Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T12:20:44.308757Z",
     "iopub.status.busy": "2025-02-07T12:20:44.308429Z",
     "iopub.status.idle": "2025-02-07T12:20:44.514444Z",
     "shell.execute_reply": "2025-02-07T12:20:44.513647Z",
     "shell.execute_reply.started": "2025-02-07T12:20:44.308739Z"
    }
   },
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "from typing import Optional, Tuple\n",
    "\n",
    "Tensor = torch.Tensor\n",
    "\n",
    "\n",
    "# 修饰器 @dataclass 会自动生成 __init__、__repr__ 和 __eq__ 等方法，减少了样板代码的编写\n",
    "@dataclass\n",
    "class AttentionOutput:\n",
    "    hidden_states: Tensor\n",
    "    attn_scores: Tensor\n",
    "\n",
    "\n",
    "class MultiHeadAttention(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]  # 隐藏层大小\n",
    "        self.num_heads = config[\"num_heads\"]  # 多头注意力的头数\n",
    "        assert (\n",
    "                self.hidden_size % self.num_heads == 0\n",
    "        ), \"Hidden size must be divisible by num_heads but got {} and {}\".format(\n",
    "            self.hidden_size, self.num_heads)\n",
    "        self.head_dim = self.hidden_size // self.num_heads  # 每个头的维度\n",
    "\n",
    "        # layers\n",
    "        # 第二个self.hidden_size可以*系数\n",
    "        self.Wq = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wk = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wv = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wo = nn.Linear(self.hidden_size, self.hidden_size, bias=False)  # 输出层\n",
    "\n",
    "    # -> Tensor 是一种类型注解，表示函数的返回值类型是 Tensor\n",
    "    def _split_heads(self, x: Tensor) -> Tensor:\n",
    "        # 若hidden_size是512\n",
    "        bs, seq_len, _ = x.shape  # [batch_size,seq_len,hidden_size]\n",
    "        # 先将x变形为[batch_size,seq_len,num_heads,head_dim]\n",
    "        x = x.view(bs, seq_len, self.num_heads, self.head_dim)\n",
    "        # num_heads是8，head_dim是64\n",
    "        return x.permute(0, 2, 1, 3)\n",
    "        # 变换维度，[batch_size, num_heads, seq_len, head_dim]\n",
    "\n",
    "    def _merge_heads(self, x: Tensor) -> Tensor:\n",
    "        # 将多头注意力的输出合并为一个张量\n",
    "        bs, _, seq_len, _ = x.shape  # [batch_size, num_heads, seq_len, head_dim]\n",
    "        # 变换维度，变为[batch_size, seq_len, hidden_size]\n",
    "        return x.permute(0, 2, 1, 3).reshape(bs, seq_len, self.hidden_size)\n",
    "\n",
    "    def forward(self, querys, keys, values, attn_mask=None) -> AttentionOutput:\n",
    "        # split heads\n",
    "        # [batch_size, seq_len,hidden_dim]->[batch_size, num_heads, seq_len, head_dim]\n",
    "        querys = self._split_heads(self.Wq(querys))\n",
    "        keys = self._split_heads(self.Wk(keys))\n",
    "        values = self._split_heads(self.Wv(values))\n",
    "\n",
    "        # calculate attention scores\n",
    "        # 计算注意力分数，matmul是矩阵乘法(在后两个维度上进行)，mT是矩阵转置,\n",
    "        # qk_logits是[batch_size, num_heads, seq_len, seq_len]\n",
    "        qk_logits = torch.matmul(querys, keys.mT)\n",
    "        if attn_mask is not None:\n",
    "            # seq_len*seq_len的部分是有效的\n",
    "            attn_mask = attn_mask[:, :, :querys.shape[-2], :keys.shape[-2]]  #切片\n",
    "            qk_logits += attn_mask * -1e9  # 给需要mask的地方设置一个负无穷\n",
    "        # 计算注意力数,softmax是在seq_len维度上进行的\n",
    "        attn_scores = F.softmax(qk_logits / (self.head_dim ** 0.5), dim=-1)\n",
    "        # attn_scores: [batch_size, num_heads, seq_len, seq_len]\n",
    "\n",
    "        # softmax后的结果与value相乘，得到新的表示\n",
    "        embeds = torch.matmul(attn_scores, values)\n",
    "        # embeds的尺寸是[batch_size, num_heads, seq_len, head_dim]\n",
    "        # _merge_heads(embeds)的输出是[batch_size, seq_len, hidden_size]\n",
    "        embeds = self.Wo(self._merge_heads(embeds))  # 输出层\n",
    "        # embeds: [batch_size, seq_len, hidden_size]\n",
    "\n",
    "        return AttentionOutput(hidden_states=embeds, attn_scores=attn_scores)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a9eb177f33d1050d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.843757Z",
     "start_time": "2025-02-06T14:04:29.835755Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T12:20:44.516122Z",
     "iopub.status.busy": "2025-02-07T12:20:44.515548Z",
     "iopub.status.idle": "2025-02-07T12:20:44.524146Z",
     "shell.execute_reply": "2025-02-07T12:20:44.523448Z",
     "shell.execute_reply.started": "2025-02-07T12:20:44.516080Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key_value.shape torch.Size([2, 4, 2])\n",
      "torch.Size([2, 3, 2])\n",
      "torch.Size([2, 2, 3, 4])\n"
     ]
    }
   ],
   "source": [
    "mha = MultiHeadAttention({\"num_heads\": 2, \"d_model\": 2})\n",
    "query = torch.randn(2, 3, 2)  # [batch_size, seq_len, hidden_size]\n",
    "query /= query.norm(dim=-1, keepdim=True)  # 归一化\n",
    "key_value = torch.randn(2, 4, 2)\n",
    "print(f'key_value.shape {key_value.shape}')\n",
    "outputs = mha(query, key_value, key_value)  #最终输出shape和query的shape一样\n",
    "print(outputs.hidden_states.shape)\n",
    "print(outputs.attn_scores.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e37f29d98cf51bac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.852319Z",
     "start_time": "2025-02-06T14:04:29.845757Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T12:20:44.525059Z",
     "iopub.status.busy": "2025-02-07T12:20:44.524859Z",
     "iopub.status.idle": "2025-02-07T12:20:44.530977Z",
     "shell.execute_reply": "2025-02-07T12:20:44.530473Z",
     "shell.execute_reply.started": "2025-02-07T12:20:44.525041Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.3384, 0.5153, 0.1463],\n",
      "        [0.2810, 0.0297, 0.6893]])\n"
     ]
    }
   ],
   "source": [
    "#随机一个2阶张量，在-1维度计算softmax\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = torch.randn(2, 3)\n",
    "x_softmax = F.softmax(x, dim=-1)\n",
    "print(x_softmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b451e28eb10aaf26",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.232694Z",
     "start_time": "2025-02-06T14:04:30.002057Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T12:20:44.531816Z",
     "iopub.status.busy": "2025-02-07T12:20:44.531624Z",
     "iopub.status.idle": "2025-02-07T12:20:44.765505Z",
     "shell.execute_reply": "2025-02-07T12:20:44.764651Z",
     "shell.execute_reply.started": "2025-02-07T12:20:44.531799Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.subplots() 用于创建子图网格，其维度基于 outputs.attn_scores.shape[:2]。子图的行数和列数似乎由 outputs.attn_scores 的前两个维度确定。\n",
    "fig, axis = plt.subplots(*outputs.attn_scores.shape[:2])\n",
    "for i in range(query.shape[0]):\n",
    "    for j in range(outputs.attn_scores.shape[1]):\n",
    "        # axis[i, j].matshow(outputs.attn_scores[i, j].detach().numpy())：此行使用 Matplotlib 的 matshow 绘制每个 i 和 j 的注意力分数热图。detach().numpy() 将 PyTorch 张量转换为 NumPy 数组以进行可视化。\n",
    "        axis[i, j].matshow(outputs.attn_scores[i, j].detach().numpy())\n",
    "        for x in range(outputs.attn_scores.shape[2]):\n",
    "            for y in range(outputs.attn_scores.shape[3]):\n",
    "                # axis[i, j].text(y, x, f\"{outputs.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\", color=\"w\")：此代码在热图上叠加文本，显示 (x, y) 位置处的注意力分数。格式化部分 f\"{outputs.attn_scores[i, j, x, y]:.2f}\" 确保以两位小数显示注意力分数。文本以白色居中显示在 (y, x) 坐标处。\n",
    "                axis[i, j].text(y, x, f\"{outputs.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\", color=\"w\")\n",
    "fig.suptitle(\"multi head attention without mask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "42dc1608bbcd426c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.480546Z",
     "start_time": "2025-02-06T14:04:30.233690Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T12:20:44.766915Z",
     "iopub.status.busy": "2025-02-07T12:20:44.766486Z",
     "iopub.status.idle": "2025-02-07T12:20:45.045476Z",
     "shell.execute_reply": "2025-02-07T12:20:45.044465Z",
     "shell.execute_reply.started": "2025-02-07T12:20:44.766879Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mask\n",
    "mask = torch.Tensor([[0, 0, 1, 1], [0, 0, 0, 1], [0, 0, 0, 0]]).reshape(1, 1, 3, 4)  #手工构造mask\n",
    "outputs_masked = mha(query, key_value, key_value, mask)\n",
    "\n",
    "fig, axis = plt.subplots(*outputs_masked.attn_scores.shape[:2])\n",
    "for i in range(query.shape[0]):\n",
    "    for j in range(outputs_masked.attn_scores.shape[1]):\n",
    "        axis[i, j].matshow(outputs_masked.attn_scores[i, j].detach().numpy())\n",
    "        for x in range(outputs_masked.attn_scores.shape[2]):\n",
    "            for y in range(outputs_masked.attn_scores.shape[3]):\n",
    "                axis[i, j].text(y, x, f\"{outputs_masked.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\",\n",
    "                                color=\"w\")\n",
    "fig.suptitle(\"multi head attention with mask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd2b7a6a479f877c",
   "metadata": {},
   "source": [
    "### Transformer-Block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2247e4861550df31",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.489814Z",
     "start_time": "2025-02-06T14:04:30.481541Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T12:20:45.046710Z",
     "iopub.status.busy": "2025-02-07T12:20:45.046395Z",
     "iopub.status.idle": "2025-02-07T12:20:45.057573Z",
     "shell.execute_reply": "2025-02-07T12:20:45.056633Z",
     "shell.execute_reply.started": "2025-02-07T12:20:45.046687Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerBlockOutput:\n",
    "    # 用于存储某个块产生的隐藏状态。\n",
    "    hidden_states: Tensor\n",
    "    # 包含了自注意力机制（self-attention）所计算得到的注意力分数\n",
    "    self_attn_scores: Tensor\n",
    "    # 是一个可选字段，存储了交叉注意力（cross-attention）计算得到的注意力分数。\n",
    "    # 这里的 Optional 表示这个字段可以是 Tensor 类型，也可以是 None。\n",
    "    cross_attn_scores: Optional[Tensor] = None\n",
    "\n",
    "\n",
    "class TransformerBlock(nn.Module):\n",
    "    def __init__(self, config, add_cross_attention=False):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]  # 隐藏层大小\n",
    "        self.num_heads = config[\"num_heads\"]  # 多头注意力的头数\n",
    "        dropout_rate = config[\"dropout\"]  # 随机失活率\n",
    "        ffn_dim = config[\"dim_feedforward\"]  # 前馈网络的维度\n",
    "        eps = config[\"layer_norm_eps\"]  # 层归一化的epsilon值\n",
    "\n",
    "        # self-attention\n",
    "        self.self_attn = MultiHeadAttention(config)  # 多头注意力\n",
    "        self.self_ln = nn.LayerNorm(self.hidden_size, eps=eps)  # 层归一化(层标准化)\n",
    "        self.self_dropout = nn.Dropout(dropout_rate)  # 随机失活\n",
    "\n",
    "        # cross-attention，交叉注意力，decoder中使用,因此额外做一个判断\n",
    "        if add_cross_attention:\n",
    "            self.cross_attn = MultiHeadAttention(config)\n",
    "            self.cross_ln = nn.LayerNorm(self.hidden_size, eps=eps)\n",
    "            self.cross_dropout = nn.Dropout(dropout_rate)\n",
    "        else:\n",
    "            self.cross_attn = None\n",
    "\n",
    "        # FFN,前馈神经网络\n",
    "        self.ffn = nn.Sequential(\n",
    "            nn.Linear(self.hidden_size, ffn_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(ffn_dim, self.hidden_size),\n",
    "        )\n",
    "        self.ffn_ln = nn.LayerNorm(self.hidden_size, eps=eps)\n",
    "        self.ffn_dropout = nn.Dropout(dropout_rate)\n",
    "\n",
    "    def forward(\n",
    "            self,\n",
    "            hidden_states,\n",
    "            attn_mask=None,\n",
    "            encoder_outputs=None,\n",
    "            cross_attn_mask=None,\n",
    "    ):\n",
    "        # self-attention,自注意力\n",
    "        self_attn_outputs = self.self_attn(\n",
    "            hidden_states, hidden_states, hidden_states, attn_mask\n",
    "        )  # self_attn_outputs 是 AttentionOutput 类型\n",
    "        self_embeds = self.self_ln(\n",
    "            hidden_states + self.self_dropout(self_attn_outputs.hidden_states)\n",
    "        )  #多头注意力进行dropout，然后和原始输入进行残差连接，然后进行层归一化\n",
    "\n",
    "        # cross-attention，交叉注意力\n",
    "        if self.cross_attn is not None:\n",
    "            assert encoder_outputs is not None, \"encoder_outputs should not be None\"\n",
    "            cross_attn_outputs = self.cross_attn(\n",
    "                self_embeds, encoder_outputs, encoder_outputs, cross_attn_mask\n",
    "            )  # query是self_embeds，key和value都是encoder_outputs\n",
    "            cross_embeds = self.cross_ln(\n",
    "                self_embeds + self.cross_dropout(cross_attn_outputs.hidden_states)\n",
    "            )  # 交叉注意力进行dropout，然后和self_embeds进行残差连接，然后进行层归一化\n",
    "\n",
    "        # FFN\n",
    "        # 如果有交叉注意力，则使用交叉注意力的输出作为FFN的输入；否则，使用self_embeds作为FFN的输入\n",
    "        embeds = cross_embeds if self.cross_attn is not None else self_embeds\n",
    "        # embeds:[batch_size, seq_len, hidden_size]\n",
    "        ffn_output = self.ffn(embeds)  # 前馈神经网络\n",
    "        # ffn_output:[batch_size, seq_len, hidden_size]\n",
    "        # 前馈神经网络进行dropout，然后和原始输入进行残差连接，然后进行层归一化\n",
    "        embeds = self.ffn_ln(embeds + self.ffn_dropout(ffn_output))\n",
    "\n",
    "        return TransformerBlockOutput(\n",
    "            hidden_states=embeds,\n",
    "            self_attn_scores=self_attn_outputs.attn_scores,\n",
    "            cross_attn_scores=cross_attn_outputs.attn_scores if self.cross_attn is not None else None,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19be62a364cb5cc2",
   "metadata": {},
   "source": [
    "## Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4f0e09edb3e7bbfe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.497908Z",
     "start_time": "2025-02-06T14:04:30.490811Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T12:20:45.058633Z",
     "iopub.status.busy": "2025-02-07T12:20:45.058368Z",
     "iopub.status.idle": "2025-02-07T12:20:45.065002Z",
     "shell.execute_reply": "2025-02-07T12:20:45.064190Z",
     "shell.execute_reply.started": "2025-02-07T12:20:45.058613Z"
    }
   },
   "outputs": [],
   "source": [
    "from typing import List\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class TransformerEncoderOutput:\n",
    "    last_hidden_states: Tensor\n",
    "    attn_scores: List[Tensor]\n",
    "\n",
    "\n",
    "class TransformerEncoder(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.num_layers = config[\"num_encoder_layers\"]\n",
    "\n",
    "        # layers,仅仅是一个模块的列表，它本身没有定义前向传递（forward pass）过程。你需要在 forward 方法中明确地定义如何使用这些模块。\n",
    "        self.layers = nn.ModuleList(\n",
    "            [TransformerBlock(config) for _ in range(self.num_layers)]\n",
    "        )\n",
    "\n",
    "    def forward(\n",
    "            self, encoder_inputs_embeds, attn_mask=None\n",
    "    ) -> TransformerEncoderOutput:\n",
    "        # 存储每个层的注意力分数\n",
    "        attn_scores = []\n",
    "        # 输入的嵌入向量作为第一层的输入(embedding+位置编码)\n",
    "        embeds = encoder_inputs_embeds\n",
    "        for layer in self.layers:\n",
    "            # layer就是一个TransformerBlock(config)\n",
    "            block_outputs = layer(embeds, attn_mask=attn_mask)\n",
    "            # 在每个层的输出中，提取了隐藏状态 block_outputs.hidden_states，\n",
    "            embeds = block_outputs.hidden_states  # 上一层的输出作为下一层的输入\n",
    "            # 并将对应的注意力分数 block_outputs.self_attn_scores 添加到列表 attn_scores 中。\n",
    "            attn_scores.append(block_outputs.self_attn_scores)  # 存储每个层的注意力分数,用于画图\n",
    "\n",
    "        return TransformerEncoderOutput(\n",
    "            last_hidden_states=embeds, attn_scores=attn_scores\n",
    "        )\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a142b31a53d27997",
   "metadata": {},
   "source": [
    "## Decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c0e7f64561cbd5f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.505801Z",
     "start_time": "2025-02-06T14:04:30.498905Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T12:20:45.066018Z",
     "iopub.status.busy": "2025-02-07T12:20:45.065769Z",
     "iopub.status.idle": "2025-02-07T12:20:45.072950Z",
     "shell.execute_reply": "2025-02-07T12:20:45.072202Z",
     "shell.execute_reply.started": "2025-02-07T12:20:45.066000Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerDecoderOutput:\n",
    "    last_hidden_states: Tensor\n",
    "    self_attn_scores: List[Tensor]\n",
    "    cross_attn_scores: List[Tensor]\n",
    "\n",
    "\n",
    "class TransformerDecoder(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.num_layers = config[\"num_decoder_layers\"]\n",
    "\n",
    "        # layers\n",
    "        self.layers = nn.ModuleList(\n",
    "            [\n",
    "                TransformerBlock(config, add_cross_attention=True)\n",
    "                for _ in range(self.num_layers)\n",
    "            ]\n",
    "        )\n",
    "\n",
    "    def forward(\n",
    "            self,\n",
    "            decoder_inputs_embeds,\n",
    "            encoder_outputs,\n",
    "            attn_mask=None,\n",
    "            cross_attn_mask=None,\n",
    "    ) -> TransformerDecoderOutput:\n",
    "        self_attn_scores = []  # 存储每个层的自注意力分数\n",
    "        cross_attn_scores = []  # 存储每个层的交叉注意力分数\n",
    "        # 输入的嵌入向量作为第一层的输入(embedding+位置编码)\n",
    "        embeds = decoder_inputs_embeds\n",
    "        for layer in self.layers:\n",
    "            # layer就是一个TransformerBlock(config, add_cross_attention=True)\n",
    "            # 为什么交叉注意力的mask要传入cross_attn_mask而不是attn_mask？\n",
    "            # 因为计算MutilHeadAttention的时候,keys和values都是encoder_outputs，query是decoder的输出，因此需要传入cross_attn_mask。\n",
    "            block_outputs = layer(\n",
    "                embeds,\n",
    "                attn_mask=attn_mask,  # 自注意力的mask\n",
    "                encoder_outputs=encoder_outputs,\n",
    "                cross_attn_mask=cross_attn_mask,  # 交叉注意力的mask\n",
    "            )\n",
    "            embeds = block_outputs.hidden_states  # 上一层的输出作为下一层的输入\n",
    "            self_attn_scores.append(block_outputs.self_attn_scores)  # 存储每个层的自注意力分数\n",
    "            cross_attn_scores.append(block_outputs.cross_attn_scores)  # 存储每个层的交叉注意力分数\n",
    "\n",
    "        return TransformerDecoderOutput(\n",
    "            last_hidden_states=embeds,\n",
    "            self_attn_scores=self_attn_scores,\n",
    "            cross_attn_scores=cross_attn_scores,\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c345d9c0e326a13",
   "metadata": {},
   "source": [
    "#### mask\n",
    "\n",
    "- mask实际上大类上只有两种\n",
    "    1. `padding_mask`：mask掉`pad_idx`，不计算损失\n",
    "    2. `attention_mask`：mask掉`pad_idx`，不计算注意力分数\n",
    "- Decoder的`attention_mask`和Encoder有一定的区别：\n",
    "    - Encoder可以同时看见序列所有信息，故只mask掉`pad_idx`\n",
    "    - Decoder只能看到在自身之前的序列的信息，故要额外mask掉自身之后的序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9baa438dbfbcdd8a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.512835Z",
     "start_time": "2025-02-06T14:04:30.506798Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T12:20:45.076331Z",
     "iopub.status.busy": "2025-02-07T12:20:45.076030Z",
     "iopub.status.idle": "2025-02-07T12:20:45.084708Z",
     "shell.execute_reply": "2025-02-07T12:20:45.083949Z",
     "shell.execute_reply.started": "2025-02-07T12:20:45.076310Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[False, False, False, False, False],\n",
      "        [ True, False, False, False, False],\n",
      "        [ True,  True, False, False, False],\n",
      "        [ True,  True,  True, False, False],\n",
      "        [ True,  True,  True,  True, False]])\n",
      "--------------------------------------------------\n",
      "tensor([[False,  True,  True,  True,  True],\n",
      "        [False, False,  True,  True,  True],\n",
      "        [False, False, False,  True,  True],\n",
      "        [False, False, False, False,  True],\n",
      "        [False, False, False, False, False]])\n"
     ]
    }
   ],
   "source": [
    "print((torch.triu(torch.ones(5, 5)) == 0))\n",
    "print(\"-\" * 50)\n",
    "# 符合Decoder的attention_mask形式\n",
    "print((torch.triu(torch.ones(5, 5)) == 0).transpose(-1, -2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "52343e686b4eed69",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.667599Z",
     "start_time": "2025-02-06T14:04:30.513830Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T12:20:45.085735Z",
     "iopub.status.busy": "2025-02-07T12:20:45.085466Z",
     "iopub.status.idle": "2025-02-07T12:20:45.242386Z",
     "shell.execute_reply": "2025-02-07T12:20:45.241126Z",
     "shell.execute_reply.started": "2025-02-07T12:20:45.085717Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1a9bodH13d3dMmjQJkyZNQmFhIZ544gnMnTtXqyRWX1u2bEG7du2wdetWtb/QkpKSquxrbW2N8PBwhIeHQ6lUYtKkSfjkk0+QmJiI9u3bq/Z788030b59e8ycORNOTk6YMWOGVrH06tULBw4cQNu2beHv7w8HBwf4+fnByckJu3fvRnZ2dpUvB33y9vaGEAJt27ZV/cFSF/7+/ti/fz9KSkrUBndUdvT7+/vrK9QqWrRoAQcHBygUilo/v3X5ndeFt7c33n77bbz99ts4e/Ys/P39sXjxYqxfv77aY5ydnTU2BV68eFGnWHSxZcsW9OvXD59++qla+a1bt6q0bNjZ2WHkyJEYOXIkysvLMXz4cMydOxcJCQlqUzoWLlwIKysrTJo0CQ4ODhgzZoxWsZSXlyO/UIHcY23g6KBbb1HJbSXaBlxEXl6e2udTH7UwAHBzc6sy2rKgoACOjo5a18IAjk7USUREBA4fPqxxNM2tW7dw//59AA/+mpPJZGp/LV64cEGrIaiaKBQKFBcXq5W5uLjAw8NDY/OUPlX+ZfrwX6K//PJLlXbsvzdBWlhYqP6y1xRjYmIipk6dioSEBKxYsUKrWHr16oULFy5g8+bNquZFCwsL9OzZE6mpqaioqKjSH6ZPw4cPh6WlJT744IMqf9kLIar8DP7uhRdegEKhUGvOKysrw5o1axAcHFylSVCfLC0tMWLECHz55Zc4ceJElfevX7+uti9Q++9cW3fv3lU1mVby9vaGg4NDrZ9fb29vnDp1Si2+3377rcZRr4+apaVlld//v/71ryq17L9/HqytrdG5c2cIIaqMlpTJZFi5ciVeeOEFREVFYceOHXWKyc5ePxsAODo6qm36SmI9evRARkaGWtnevXvRo0ePOp1H8jWxpUuX4tatW6oBDl9//TUuX74M4MFf+JV9AY/CtGnTsGPHDgwZMgQvv/wyAgICUFpaiuPHj2PLli24cOECmjdvjsGDByM1NRUDBw7EmDFjUFhYiGXLlqF9+/Z17ocBHswRa9WqFV544QX4+fnB3t4e+/btw5EjR6rM69K3IUOGYOvWrXj++ecxePBg5ObmIj09HZ07d1ZrdnjllVdw48YNPP3002jVqhUuXryIjz/+GP7+/nj88cc1nnvhwoUoLi5GTEwMHBwc1AZfaFKZoE6fPo158+apynv37o1du3ap5gU9Kt7e3pgzZw4SEhJw4cIFDBs2DA4ODsjNzcW2bdvw2muvqSYxaxIcHIwXX3wRCQkJKCwsRPv27bFu3TpcuHChyl/1j8L8+fOxf/9+BAcH49VXX0Xnzp1x48YNZGdnY9++fbhx4wYA7X/n2jpz5gz69++PiIgIdO7cGVZWVti2bRsKCgrUBqloMn78eKSmpiIsLAwTJkxAYWEh0tPT4ePjg5KSknr9HHQ1ZMgQzJ49G9HR0ejZsyeOHz+ODRs2VOmfHjBgANzc3BASEgJXV1ecPHkSS5cuxeDBg+Hg4FDlvBYWFli/fj2GDRuGiIgIfPvtt3j66acb6rbq7M6dOzh37pzqdW5uLnJyctC0aVO0bt0aCQkJuHLlCj777DMAD6YJLV26FO+88w7Gjx+P77//Hl988YXGaUc1qtNYRiPUpk0bAUDjlpubW+OxUVFRws7Orkp5nz59NA6FbdOmjRg8eLBa2e3bt0VCQoJo3769sLa2Fs2bNxc9e/YUixYtEuXl5ar9Pv30U9GhQwchl8tFp06dxJo1a6oM2RbiwdBUTUPn27RpoxrqXlZWJqZNmyb8/PyEg4ODsLOzE35+fmL58uU13q8Q6kN46/OzUCqVYt68eaJNmzZCLpeLbt26iZ07d1YZ4rxlyxYxYMAA4eLiIqytrUXr1q3F66+/Lq5du6ba5+Eh9pUUCoUYPXq0sLKyEtu3b6/1flxcXAQAUVBQoCo7dOiQACB69epV6/08fP+ahmjXNMS+0pdffimeeuopYWdnJ+zs7ESnTp1ETEyMOH36dK3x//XXX2Lq1KnCzc1NyOVy0b17d7F79+5aj6vpXjR9ToXQ/NkqKCgQMTExwtPTUzRq1Ei4ubmJ/v37i5UrV6r20fZ3Xvkz0zR0Hg8NSy8qKhIxMTGiU6dOws7OTjg5OYng4GDxxRdfaHXf69evF+3atRPW1tbC399f7Nmzp96xCKH798C9e/fE22+/Ldzd3YWtra0ICQkRhw8frjId4JNPPhG9e/cWzZo1E3K5XHh7e4tp06aJ4uJi1T6a/v+8e/eu6NOnj7C3t1ebNqFJcXGxACDyT7cWd6966bTln24tAKjFV5P9+/dr/B6u/N6KioqqMj1i//79wt/fX1hbW4t27drVaYpUJZkQdezNJiIio1RSUgInJydcPd1KL31iHh0vo7i4uMqEfGPCPjEiIpIsyfeJERGROoUQUOjYyKbr8Q2FSYyIyMQoIaCEbklI1+MbCpsTiYhIslgTIyIyMUoIKMykJsYkRkRkYticSEREJAGsiRERmRhzGp3ImhiAZcuWwcvLCzY2NggODkZWVpbBYklOTkb37t3h4OAAFxcXDBs2TLVmlrGYP38+ZDIZ4uLiDBrHlStX8NJLL6FZs2awtbVFly5dcPToUYPGpFAokJiYiLZt28LW1hbe3t748MMP6/yEfF3Utky8EAIzZ86Eu7s7bG1tERoairNnzxospoqKCkyfPh1dunSBnZ0dPDw8EBkZWe+18vQR099NnDgRMplMbcVrY6bU0yYFZp/ENm/ejPj4eCQlJSE7Oxt+fn4ICwtTW0upIf3www+IiYnBzz//jL1796KiogIDBgxAaWmpQeL5uyNHjuCTTz5RPczXUG7evImQkBA0atQIu3btwh9//IHFixcbfPHAlJQUrFixAkuXLsXJkyeRkpKCBQsW4OOPP26wGGpbJn7BggX46KOPkJ6ejl9++QV2dnYICwur8lDehorp7t27yM7ORmJiIrKzs7F161acPn0azz333COLp7aYHrZt2zb8/PPPWi2NYywU/x3YoesmCXV+UJWJCQoKUnuenEKhEB4eHiI5OdmAUf1PYWGhACB++OEHQ4cibt++LTp06CD27t0r+vTpI6ZMmWKwWKZPny6eeuopg12/OoMHDxbjx49XKxs+fLgYO3asQeLB35aJVyqVws3NTe2Zgrdu3RJyuVx8/vnnBolJk6ysLAFAXLx40aAxXb58WbRs2VKcOHFCtGnTRvzjH/9okHjqq/LZif8+6SIuXXbTafv3SZc6PTvRUMy6JlZeXo5jx46pradkYWGB0NDQei8zoW+VS640bdrUwJEAMTExGDx4cK3rTzWEHTt2IDAwEC+++CJcXFzQrVs3rFq1ytBhoWfPnsjIyMCZM2cAPFgm5NChQ490jbe6yM3NRX5+vtrv0MnJCcHBwUbzmQcefO5lMhmaNGlisBiUSiXGjRuHadOmwcfHx2Bx1IdC6GeTArMe2FFUVASFQqFxiexTp04ZKKr/USqViIuLQ0hICHx9fQ0ay6ZNm5CdnY0jR44YNI5K58+fx4oVKxAfH493330XR44cweTJk2FtbV3tSsUNYcaMGSgpKUGnTp1gaWkJhUKBuXPnYuzYsQaL6WH5+fkAoPEzX/meod27dw/Tp0/H6NGjDfrg2ZSUFFhZWWHy5MkGi6G+9NGnJZU+MbNOYsYuJiYGJ06cwKFDhwwaR15eHqZMmYK9e/eqrT5rSEqlEoGBgap1xLp164YTJ04gPT3doEnsiy++wIYNG7Bx40b4+PggJycHcXFx8PDwMGhcUlFRUYGIiAgIIbReHPVROHbsGJYsWYLs7Gy11azJ+Jh1c2Lz5s1haWmpcYlsNzc3A0X1QGxsLHbu3In9+/ejVatWBo3l2LFjKCwsxBNPPAErKytYWVnhhx9+wEcffQQrKyu1Fasbiru7Ozp37qxW9vjjj+PSpUsNHsvDpk2bhhkzZmDUqFHo0qULxo0bh7feegvJyckGjatS5efaGD/zlQns4sWL2Lt3r0FrYQcPHkRhYSFat26t+sxfvHgRb7/9Nry8vAwWl7aUkEGh46aENJK3WScxa2trBAQEqC2RrVQqkZGRUeclsvVFCIHY2Fhs27YN33//Pdq2bWuQOB7Wv39/HD9+HDk5OaotMDAQY8eORU5Ojmr5+oYUEhJSZerBmTNn0KZNmwaP5WF3796FhYX6/1aWlpZQKo2jcaZt27Zwc3NT+8yXlJTgl19+MdhnHvhfAjt79iz27duHZs2aGSwWABg3bhx+//13tc+8h4cHpk2bhj179hg0Nm0ohX42KTD75sT4+HhERUUhMDAQQUFBSEtLQ2lpKaKjow0ST0xMDDZu3IivvvoKDg4Oqn4KJycn2NraGiQmBweHKn1ydnZ2aNasmcH66t566y307NkT8+bNQ0REBLKysrBy5UqsXLnSIPFUCg8Px9y5c9G6dWv4+Pjg119/RWpqKsaPH99gMdS2THxcXBzmzJmDDh06oG3btkhMTISHhweGDRtmkJjc3d3xwgsvIDs7Gzt37oRCoVB97ps2bQpra+sGj6l169ZVEmmjRo3g5uaGjh07PpJ4qJ4MPTzSGHz88ceidevWwtraWgQFBdW6BPijBA3LewOo17Ldj5Khh9gLIcTXX38tfH19hVwuF506dRIrV640aDxCCFFSUiKmTJkiWrduLWxsbES7du3Ee++9J8rKyhoshtqWiVcqlSIxMVG4uroKuVwu+vfvL06fPm2wmHJzc6v93O/fv98gMWkipSH2v/zbTfz7kodO2y//dpPEEHuZEBJ5tggREdWopKQETk5O+Onf7rB30K236M5tJXr6XENxcbFB+ydrY9Z9YkREJG1m3ydGRGRqlEIGpdBtdKGuxzcUJjEiIhNTOUxe13NIAZsTiYhIslgTIyIyMQpYQKFjHaXhH2FQP0xiREQmRuihT0ywT4yIiAyBfWJmpqysDLNmzUJZWZmhQ1ExxpgA44yLMWmHMWnPWOOiqjjZGf+bIGhMk/qMMSbAOONiTNphTNoz1rhqUxn3rt/bwk7Hyc6lt5UY1DXX6H8GbE4kIjIxSsig1LGhTQlp1G/YnEhERJJl8jUxpVKJq1evwsHBodrF7UpKStT+awyMMSbAOONiTNphTNpr6LiEELh9+zY8PDyqLOVTH+Y0sMPk+8QuX74MT09PQ4dBRFSrvLw8nRbBrewT2/ZbB9g56LbOX+ltBZ73O8s+MUNzcHAAADyFZ2GFRjqfb9uZ4zqfg4joYSV3lGjzxAXV9xVpz+STWGUTohUawUqmexJz1HHEDxFRdarr8qirBwM7dHwAsESaE00+iRERmRulHh47xdGJREREj5gkktiyZcvg5eUFGxsbBAcHIysry9AhEREZLYWw0MsmBUYf5ebNmxEfH4+kpCRkZ2fDz88PYWFhKCwsNHRoRERGSQkLvWxSYPRRpqam4tVXX0V0dDQ6d+6M9PR0NG7cGKtXrzZ0aEREZGBGPbCjvLwcx44dQ0JCgqrMwsICoaGhOHz4sMZjysrK1B7aaWyTKImIHjWFkEGh41Iquh7fUIy6JlZUVASFQgFXV1e1cldXV+Tn52s8Jjk5GU5OTqqNE52JyNxULoqp6yYF0oiyDhISElBcXKza8vLyDB0SEVGDUgoLvWxSYNTNic2bN4elpSUKCgrUygsKCuDm5qbxGLlcDrlc3hDhERGRgRl1qrW2tkZAQAAyMjJUZUqlEhkZGejRo4cBIyMiMl7m1Jxo1DUxAIiPj0dUVBQCAwMRFBSEtLQ0lJaWIjo62tChEREZJSV0H5ih1E8oj5zRJ7GRI0fi+vXrmDlzJvLz8+Hv74/du3dXGexBRETmx+iTGADExsYiNjbW0GEQEUmCPiYrS2WysySSGBERaU8fj43iY6eIiIgeMdbE6ijMw18v59lzNUcv5yEi+juuJ0ZERJLF5kQiIiIJYE2MiMjE6GOyMic7ExGRQSiFDEpdJzvzKfZERESPFmtiREQmRqmH5kROdiYiIoPQx1IqXIqFiIgMQgEZFDrO89L1+IYijVRLRESkAWtiREQmhs2JREQkWQro3hyo0E8oj5w0Ui0REZEGrIkREZkYNicSEZFk8QHAREREdbRs2TJ4eXnBxsYGwcHByMrKqnH/tLQ0dOzYEba2tvD09MRbb72Fe/fu1emaTGJERCZG/Hc9MV02UceBIZs3b0Z8fDySkpKQnZ0NPz8/hIWFobCwUOP+GzduxIwZM5CUlISTJ0/i008/xebNm/Huu+/W6bpMYkREJqayOVHXrS5SU1Px6quvIjo6Gp07d0Z6ejoaN26M1atXa9z/p59+QkhICMaMGQMvLy8MGDAAo0ePrrX29nfsEzMQfa0QDXCVaCJ6dEpKStRey+VyyOVytbLy8nIcO3YMCQkJqjILCwuEhobi8OHDGs/bs2dPrF+/HllZWQgKCsL58+fx7bffYty4cXWKj0mMiMjE6HMpFk9PT7XypKQkzJo1S62sqKgICoUCrq6uauWurq44deqUxvOPGTMGRUVFeOqppyCEwP379zFx4sQ6NycyiRERmRh9LoqZl5cHR0dHVfnfa2H1lZmZiXnz5mH58uUIDg7GuXPnMGXKFHz44YdITEzU+jxMYkREVC1HR0e1JKZJ8+bNYWlpiYKCArXygoICuLm5aTwmMTER48aNwyuvvAIA6NKlC0pLS/Haa6/hvffeg4WFdkmYAzuIiExMZXOirpu2rK2tERAQgIyMjP/FoFQiIyMDPXr00HjM3bt3qyQqS0tLAIAQQutrsyZGRGRilLDQeVHLuh4fHx+PqKgoBAYGIigoCGlpaSgtLUV0dDQAIDIyEi1btkRycjIAIDw8HKmpqejWrZuqOTExMRHh4eGqZKYNo05iycnJ2Lp1K06dOgVbW1v07NkTKSkp6Nixo6FDIyIyWgohg0LHgR11PX7kyJG4fv06Zs6cifz8fPj7+2P37t2qwR6XLl1Sq3m9//77kMlkeP/993HlyhW0aNEC4eHhmDt3bp2uKxN1qbc1sIEDB2LUqFHo3r077t+/j3fffRcnTpzAH3/8ATs7O63OUVJSAicnJ/TFUFjJGj3iiA2DQ+yJpK3kthLOj51HcXFxrf1PNZ7nv993bxwcDrm9bt93ZXcqsKLXVp1jetSMuia2e/dutddr166Fi4sLjh07ht69exsoKiIi46bPIfbGzqiT2N8VFxcDAJo2bVrtPmVlZSgrK1O9/vtEPSIiUyf08BR7wQcA65dSqURcXBxCQkLg6+tb7X7JyclwcnJSbX+fqEdERKZDMkksJiYGJ06cwKZNm2rcLyEhAcXFxaotLy+vgSIkIjIOCsj0skmBJJoTY2NjsXPnThw4cACtWrWqcV9Nz/UiIjInSqF7n5bSaIf8qTPqJCaEwJtvvolt27YhMzMTbdu2NXRIRERkRIw6icXExGDjxo346quv4ODggPz8fACAk5MTbG1tDRwdEZFxUuphYIeuxzcUo45yxYoVKC4uRt++feHu7q7aNm/ebOjQiIiMlq4LYlZuUmDUNTEjnodNRERGwKiTGBER1Z0hHjtlKExiREQmxpz6xJjETECYh79ezsNnMBKR1DCJERGZGCX08OxEDuwgIiJDEHoYXSiYxIiIyBDM6Sn20ui5IyIi0oA1MSIiE8PRiUREJFlsTiQiIpIA1sSIiEyMPp59yCH2RERkEGxOJCIikgDWxIiITIw51cSYxIiITIw5JTE2JxIRkWSxJkZEZGLMqSbGJEZEZGIEdB8iL/QTyiPHJEZEZGLMqSbGPjEiIpIs1sRIRV8rRANcJZrIkMypJsYkRkRkYswpibE5kYiIJIs1MSIiE2NONTEmMSIiEyOEDELHJKTr8Q2FzYlERCRZkkpi8+fPh0wmQ1xcnKFDISIyWpXriem6SYFkmhOPHDmCTz75BF27djV0KERERs2c+sQkURO7c+cOxo4di1WrVsHZ2dnQ4RARkZGQRBKLiYnB4MGDERoaWuu+ZWVlKCkpUduIiMxJ5cAOXTcpMPrmxE2bNiE7OxtHjhzRav/k5GR88MEHjzgqIiLjxeZEI5GXl4cpU6Zgw4YNsLGx0eqYhIQEFBcXq7a8vLxHHCURERmKUdfEjh07hsLCQjzxxBOqMoVCgQMHDmDp0qUoKyuDpaWl2jFyuRxyubyhQyUiMhrmNE/MqJNY//79cfz4cbWy6OhodOrUCdOnT6+SwIiI6EEC0rU5kElMDxwcHODr66tWZmdnh2bNmlUpJyKiBwQAoeOqllJZFNOo+8SIiIhqYtQ1MU0yMzMNHQIRkVFTQgaZjk/c4BM7iIjIIMxpYAebE4mISLJYE6NHIszDX2/n2nM1R2/nIjIHSiGDzEwmOzOJERGZGCH0MDpRIsMT2ZxIRESSxZoYEZGJMaeBHUxiREQmxpySGJsTiYhIslgTIyIyMRydSEREksXRiURERBLAmhgRkYl5UBPTdWCHnoJ5xJjEiIhMjDmNTmQSIyIyMQK6rwcmkYoY+8SIiEi6WBMjIjIxbE4kIiLpMqP2RDYnEhGRXixbtgxeXl6wsbFBcHAwsrKyatz/1q1biImJgbu7O+RyOR577DF8++23dboma2JERKZGD82JqOPxmzdvRnx8PNLT0xEcHIy0tDSEhYXh9OnTcHFxqbJ/eXk5nnnmGbi4uGDLli1o2bIlLl68iCZNmtTpukxiREQmxhBP7EhNTcWrr76K6OhoAEB6ejq++eYbrF69GjNmzKiy/+rVq3Hjxg389NNPaNSoEQDAy8urznGyOZGIiKpVUlKitpWVlVXZp7y8HMeOHUNoaKiqzMLCAqGhoTh8+LDG8+7YsQM9evRATEwMXF1d4evri3nz5kGhUNQpPtbEyOiFefjr5Tx7rubo5TxExk6foxM9PT3VypOSkjBr1iy1sqKiIigUCri6uqqVu7q64tSpUxrPf/78eXz//fcYO3Ysvv32W5w7dw6TJk1CRUUFkpKStI6TSYyIyNQIWZ37tDSeA0BeXh4cHR1VxXK5XLfz/pdSqYSLiwtWrlwJS0tLBAQE4MqVK1i4cCGTGBER6Yejo6NaEtOkefPmsLS0REFBgVp5QUEB3NzcNB7j7u6ORo0awdLSUlX2+OOPIz8/H+Xl5bC2ttYqPvaJERGZmMqBHbpu2rK2tkZAQAAyMjJUZUqlEhkZGejRo4fGY0JCQnDu3DkolUpV2ZkzZ+Du7q51AgOYxIiITI/Q01YH8fHxWLVqFdatW4eTJ0/ijTfeQGlpqWq0YmRkJBISElT7v/HGG7hx4wamTJmCM2fO4JtvvsG8efMQExNTp+safRK7cuUKXnrpJTRr1gy2trbo0qULjh49auiwiIjoISNHjsSiRYswc+ZM+Pv7IycnB7t371YN9rh06RKuXbum2t/T0xN79uzBkSNH0LVrV0yePBlTpkzROBy/JkbdJ3bz5k2EhISgX79+2LVrF1q0aIGzZ8/C2dnZ0KERERktQz07MTY2FrGxsRrfy8zMrFLWo0cP/Pzzz3W+zsOMOomlpKTA09MTa9asUZW1bdvWgBEREUmERJ59qCujbk7csWMHAgMD8eKLL8LFxQXdunXDqlWrDB0WEZFRq6yJ6bpJgVEnsfPnz2PFihXo0KED9uzZgzfeeAOTJ0/GunXrqj2mrKysygxzIiIyTUbdnKhUKhEYGIh58+YBALp164YTJ04gPT0dUVFRGo9JTk7GBx980JBhEhEZFy7FYhzc3d3RuXNntbLHH38cly5dqvaYhIQEFBcXq7a8vLxHHSYRkZGR6WkzfkZdEwsJCcHp06fVys6cOYM2bdpUe4xcLtfbY1GIiMi4GXVN7K233sLPP/+MefPm4dy5c9i4cSNWrlxZ58lwRERmxQCTnQ3FqJNY9+7dsW3bNnz++efw9fXFhx9+iLS0NIwdO9bQoRERGS8zSmJG3ZwIAEOGDMGQIUMMHQYRERmhetXE8vLycPnyZdXrrKwsxMXFYeXKlXoLjIiI6qlyKRZdNwmoVxIbM2YM9u/fDwDIz8/HM888g6ysLLz33nuYPXu2XgMkIqK6aein2BtSvZoTT5w4gaCgIADAF198AV9fX/z444/47rvvMHHiRMycOVOvQRLpg75WiAa4SjSRsahXEquoqFANY9+3bx+ee+45AECnTp3UnlJMREQGwMnONfPx8UF6ejoOHjyIvXv3YuDAgQCAq1evolmzZnoNkIiI6oh9YjVLSUnBJ598gr59+2L06NHw8/MD8OCBvZXNjERERI9avZoT+/bti6KiIpSUlKit7fXaa6+hcePGeguOiIjqTiYebLqeQwrqVRNLSkrC5cuXqyxO6eXlBRcXF70ERkRE9WRGk53rlcS++uoreHt7o3///ti4cSPKysr0HRcREdUX+8RqlpOTgyNHjsDHxwdTpkyBm5sb3njjDRw5ckTf8REREVWr3s9O7NatGz766CNcvXoVn376KS5fvoyQkBB07doVS5YsQXFxsT7jJCIibbE5UXtCCFRUVKC8vBxCCDg7O2Pp0qXw9PTE5s2b9REjERHVBZNY7Y4dO4bY2Fi4u7vjrbfeQrdu3XDy5En88MMPOHv2LObOnYvJkyfrM1YiIiI19UpiXbp0wZNPPonc3Fx8+umnyMvLw/z589G+fXvVPqNHj8b169f1FigREWnJjGpi9ZonFhERgfHjx6Nly5bV7tO8eXMolcp6B0ZERPWkj9GFpjo6saKiAmvXrkVJScmjiIeIiEhrda6JNWrUCPfu3XsUsRARkR7wiR21iImJQUpKCu7fv6/veIiISFfsE6vZkSNHkJGRge+++w5dunSBnZ2d2vtbt27VS3BEREQ1qVcSa9KkCUaMGKHvWIiIiOqkXklszZo1+o6DiIj0RAY99InpJZJHr15JDADu37+PzMxM/PnnnxgzZgwcHBxw9epVODo6wt7eXp8xEhmdMA9/vZ1rz9UcvZ2LyNzUK4ldvHgRAwcOxKVLl1BWVoZnnnkGDg4OSElJQVlZGdLT0/UdJxERaYvzxGo2ZcoUBAYG4ubNm7C1tVWVP//888jIyNBbcEREVA8cnVizgwcP4qeffoK1tbVauZeXF65cuaKXwIiIqJ70kYQkksTqVRNTKpVQKBRVyi9fvgwHBwedgyIiItJGvZLYgAEDkJaWpnotk8lw584dJCUl4dlnn9VXbEREVA+VT+zQdZOCeiWxxYsX48cff0Tnzp1x7949jBkzRtWUmJKSorfgFAoFEhMT0bZtW9ja2sLb2xsffvghhJDIT5eIyBDYJ1azVq1a4bfffsOmTZvw+++/486dO5gwYQLGjh2rNtBDVykpKVixYgXWrVsHHx8fHD16FNHR0XBycuJaZUREVP95YlZWVnjppZf0GUsVP/30E4YOHYrBgwcDeDBw5PPPP0dWVtYjvS4RkaSZ0cCOeiWxzz77rMb3IyMj6xXM3/Xs2RMrV67EmTNn8Nhjj+G3337DoUOHkJqaWu0xZWVlKCsrU73mkjFEZG7M6Sn29UpiU6ZMUXtdUVGBu3fvwtraGo0bN9ZbEpsxYwZKSkrQqVMnWFpaQqFQYO7cuRg7dmy1xyQnJ+ODDz7Qy/WJiMi41Wtgx82bN9W2O3fu4PTp03jqqafw+eef6y24L774Ahs2bMDGjRuRnZ2NdevWYdGiRVi3bl21xyQkJKC4uFi15eXl6S0eIiJJqHxih66bBNS7T+zvOnTogPnz5+Oll17CqVOn9HLOadOmYcaMGRg1ahQAoEuXLrh48SKSk5MRFRWl8Ri5XA65XK6X6xMRSZIZ9YnVqyZWHSsrK1y9elVv57t79y4sLNRDtLS0hFKp1Ns1iIhIuupVE9uxY4faayEErl27hqVLlyIkJEQvgQFAeHg45s6di9atW8PHxwe//vorUlNTMX78eL1dg4jI1HBgRy2GDRum9lomk6FFixZ4+umnsXjxYn3EBQD4+OOPkZiYiEmTJqGwsBAeHh54/fXXMXPmTL1dg4jI5JhRc2K9klhDNec5ODggLS1N7RFXRERUC308NsqUk1h8fLzW+9Y0p4uIiEgX9Upiv/76K7Kzs3H//n107NgRAHDmzBlYWlriiSeeUO0nk0ljiCYRkUlhc2LNwsPD4eDggHXr1sHZ2RnAg7lj0dHR6NWrF95++229BklkysI8/PVynj1Xc/RyHjIBZpTE6v0U++TkZFUCAwBnZ2fMmTNHrwM7iIiIalKvmlhJSQmuX79epfz69eu4ffu2zkEREVH9mdMQ+3rVxJ5//nlER0dj69atuHz5Mi5fvowvv/wSEyZMwPDhw/UdIxERkUb1qomlp6dj6tSpGDNmDCoqKh6cyMoKEyZMwMKFC/UaIBERUXXqlcQaN26M5cuXY+HChfjzzz8BAN7e3rCzs9NrcEREVA9mNLBDpwcA29nZoWvXrvqKhYiI9IB9YkRERBKgt6VYiIjIiEikJqUrJjEiIlNjRn1ibE4kIiLJYk2MiMjEmNPADiYxIiJTY0bNiUxiREQmxpxqYuwTIyIiyWISIyIyNUJPWx0tW7YMXl5esLGxQXBwMLKysrQ6btOmTZDJZBg2bFidr8kkRkRkagyQxDZv3oz4+HgkJSUhOzsbfn5+CAsLQ2FhYY3HXbhwAVOnTkWvXr3qdsH/YhIjIiKdpaam4tVXX0V0dDQ6d+6M9PR0NG7cGKtXr672GIVCgbFjx+KDDz5Au3bt6nVdDuwgMhH6WiEa4CrRUqfPgR0lJSVq5XK5HHK5XK2svLwcx44dQ0JCgqrMwsICoaGhOHz4cLXXmD17NlxcXDBhwgQcPHiwXnGyJkZEZGr02Jzo6ekJJycn1ZacnFzlckVFRVAoFHB1dVUrd3V1RX5+vsYQDx06hE8//RSrVq3S6VZZEyMiomrl5eXB0dFR9frvtbD6uH37NsaNG4dVq1ahefPmOp2LSYyIyNTocbKzo6OjWhLTpHnz5rC0tERBQYFaeUFBAdzc3Krs/+eff+LChQsIDw9XlSmVSgAPFlg+ffo0vL29tQqTzYlERCamsk9M101b1tbWCAgIQEZGhqpMqVQiIyMDPXr0qLJ/p06dcPz4ceTk5Ki25557Dv369UNOTg48PT21vjZrYkREpLP4+HhERUUhMDAQQUFBSEtLQ2lpKaKjowEAkZGRaNmyJZKTk2FjYwNfX1+145s0aQIAVcprY9Ca2IEDBxAeHg4PDw/IZDJs375d7X0hBGbOnAl3d3fY2toiNDQUZ8+eNUywRERSYYB5YiNHjsSiRYswc+ZM+Pv7IycnB7t371YN9rh06RKuXbum+739jUFrYqWlpfDz88P48eMxfPjwKu8vWLAAH330EdatW4e2bdsiMTERYWFh+OOPP2BjY2OAiImIjJ+hnp0YGxuL2NhYje9lZmbWeOzatWvrfkEYOIkNGjQIgwYN0vieEAJpaWl4//33MXToUADAZ599BldXV2zfvh2jRo1qyFCJiMgIGe3AjtzcXOTn5yM0NFRV5uTkhODg4Bonz5WVlaGkpERtIyIyKwZ6dqIhGG0Sq5wgV5fJcwCQnJysNjGvLqNciIhMApOYdCUkJKC4uFi15eXlGTokIqIGJdPTJgVGm8QqJ8hpO3muklwuV03O02aSHhERSZfRJrG2bdvCzc1NbfJcSUkJfvnlF42T54iI6L/MqDnRoKMT79y5g3Pnzqle5+bmIicnB02bNkXr1q0RFxeHOXPmoEOHDqoh9h4eHvVaOI2IyFwYaoi9IRg0iR09ehT9+vVTvY6PjwcAREVFYe3atXjnnXdQWlqK1157Dbdu3cJTTz2F3bt3c44YEREBMHAS69u3L4SoPt3LZDLMnj0bs2fPbsCoiIgkTo8PADZ2fHYiEZEpkkgS0pXRDuwgIiKqDWtiRFRFmIe/Xs6z52qOXs5DdcOBHUREJF1m1CfG5kQiIpIs1sSIiEwMmxOJiEi62JxIRERk/FgTIyIyMWxOJCIi6TKj5kQmMSIiU2NGSYx9YkREJFmsiRERmRj2iRERkXSxOZGIiMj4sSZGRGRiZEJAVsNajdqeQwqYxIiITA2bE4mIiIwfa2JERCaGoxOJiEi6zKg5kUmMiB4Zfa0QDXCVaNKMSYyIyMSwOZGIiKTLjJoTOTqRiIgkizUxIiITw+ZEIiKSLjYnNowDBw4gPDwcHh4ekMlk2L59u+q9iooKTJ8+HV26dIGdnR08PDwQGRmJq1evGi5gIiKJqKyN1XeTCoMmsdLSUvj5+WHZsmVV3rt79y6ys7ORmJiI7OxsbN26FadPn8Zzzz1ngEiJiMgYGbQ5cdCgQRg0aJDG95ycnLB37161sqVLlyIoKAiXLl1C69atGyJEIiLpEeLBpus5JEBSfWLFxcWQyWRo0qRJtfuUlZWhrKxM9bqkpKQBIiMiMh7mNLBDMkPs7927h+nTp2P06NFwdHSsdr/k5GQ4OTmpNk9PzwaMkoiIGpIkklhFRQUiIiIghMCKFStq3DchIQHFxcWqLS8vr4GiJCIyEkJPmwQYfXNiZQK7ePEivv/++xprYQAgl8shl8sbKDoiIuMjUz7YdD2HFBh1EqtMYGfPnsX+/fvRrFkzQ4dERERGxKBJ7M6dOzh37pzqdW5uLnJyctC0aVO4u7vjhRdeQHZ2Nnbu3AmFQoH8/HwAQNOmTWFtbW2osImIjJsZTXY2aBI7evQo+vXrp3odHx8PAIiKisKsWbOwY8cOAIC/v7/acfv370ffvn0bKkwiIkkxp9GJBk1iffv2hahhLkJN7xERERl1nxgREdUDJzsTEZFUsTmRiMjIhHn46+1ce67m6O1cZFhMYkREpoajE4mISKrYnEhERNJlRgM7JPHsRCIiIk1YEyMiMjFsTiQiIukyo4EdbE4kIiLJYk2MiMjEsDmRiIikSykebLqeQwLYnEhERJLFmhgRkakxo4EdTGJERCZGBj30ieklkkePzYlERCRZrIkREZkaPnaKiIikqnKIva5bXS1btgxeXl6wsbFBcHAwsrKyqt131apV6NWrF5ydneHs7IzQ0NAa968OkxgRkakRetrqYPPmzYiPj0dSUhKys7Ph5+eHsLAwFBYWatw/MzMTo0ePxv79+3H48GF4enpiwIABuHLlSp2uyyRGREQ6S01Nxauvvoro6Gh07twZ6enpaNy4MVavXq1x/w0bNmDSpEnw9/dHp06d8M9//hNKpRIZGRl1ui6TGBGRiZEJoZcNAEpKStS2srKyKtcrLy/HsWPHEBoaqiqzsLBAaGgoDh8+rFXMd+/eRUVFBZo2bVqne+XADiIyO2Ee/no5z56rOXo5j94p/7vpeg4Anp6easVJSUmYNWuWWllRUREUCgVcXV3Vyl1dXXHq1CmtLjd9+nR4eHioJUJtMIkREVG18vLy4OjoqHotl8v1fo358+dj06ZNyMzMhI2NTZ2OZRIjIjIxDzcH6nIOAHB0dFRLYpo0b94clpaWKCgoUCsvKCiAm5tbjccuWrQI8+fPx759+9C1a9c6x8k+MSIiU9PAoxOtra0REBCgNiijcpBGjx49qj1uwYIF+PDDD7F7924EBgbW4Qb/hzUxIiLSWXx8PKKiohAYGIigoCCkpaWhtLQU0dHRAIDIyEi0bNkSycnJAICUlBTMnDkTGzduhJeXF/Lz8wEA9vb2sLe31/q6Bq2JHThwAOHh4fDw8IBMJsP27dur3XfixImQyWRIS0trsPiIiCSp8okdum51MHLkSCxatAgzZ86Ev78/cnJysHv3btVgj0uXLuHatWuq/VesWIHy8nK88MILcHd3V22LFi2q03UNWhMrLS2Fn58fxo8fj+HDh1e737Zt2/Dzzz/Dw8OjAaMjIpImQy2KGRsbi9jYWI3vZWZmqr2+cOFC3S+ggUGT2KBBgzBo0KAa97ly5QrefPNN7NmzB4MHD26gyIiISAqMuk9MqVRi3LhxmDZtGnx8fLQ6pqysTG0yXklJyaMKj4jIOPEBwMYhJSUFVlZWmDx5stbHJCcnw8nJSbX9faIeEZGpkyn1s0mB0SaxY8eOYcmSJVi7di1kMu2XZ0tISEBxcbFqy8vLe4RREhGRIRltEjt48CAKCwvRunVrWFlZwcrKChcvXsTbb78NLy+vao+Ty+WqyXnaTNIjIjI5BhidaChG2yc2bty4Ks/QCgsLw7hx41TzDoiISIN6LKWi8RwSYNAkdufOHZw7d071Ojc3Fzk5OWjatClat26NZs2aqe3fqFEjuLm5oWPHjg0dKhGRZOjzsVPGzqBJ7OjRo+jXr5/qdXx8PAAgKioKa9euNVBUREQkFQZNYn379oWoQ7bX1+Q4IiKTZkZD7I22T4yIiOpJQPf1xKSRw4x3dCIREVFtWBMjIqonfa0QfV9UADivl3MBHNhBRERSJqCHPjG9RPLIsTmRiIgkizUxIiJTw9GJREQkWUoA2j9ytvpzSACbE4mISLJYEyMiMjEcnUhERNJlRn1ibE4kIiLJYk2MiMjUmFFNjEmMiMjUMIkREZFkcYg9ERGR8WNNjIjIxHCIPRERSZcZ9YmxOZGIiCSLNTEiIlOjFIBMx5qUUho1MSYxIiJTY0bNiSafxMR/fxH3USGZRd6IyLzcRwWA/31fkfZMPondvn0bAHAI3xo4EiKimt2+fRtOTk56OJMeamIS+avf5JOYh4cH8vLy4ODgAJlM8+y/kpISeHp6Ii8vD46Ojg0coWbGGBNgnHExJu0wJu01dFxCCNy+fRseHh76OiGbE02FhYUFWrVqpdW+jo6ORvU/EmCcMQHGGRdj0g5j0l5DxqWfGpj5MfkkRkRkdpQCOjcHcnQiEREZhFA+2HQ9hwRwsjMAuVyOpKQkyOVyQ4eiYowxAcYZF2PSDmPSnrHGRVXJBMd0EhGZhJKSEjg5OSHU8w1YWeiWgO8ry7AvbwWKi4uNsr+yEpsTiYhMDfvEiIhIssxoiD37xIiISLJYEyMiMjUCeqiJ6SWSR441MTJbffv2RVxcnKHDINK/yuZEXTcJYBIjIiLJYnMiEZGpUSoB6DhZWcnJzkSS8s0338DJyQkbNmxAXl4eIiIi0KRJEzRt2hRDhw7FhQsXAAAHDhxAo0aNkJ+fr3Z8XFwcevXqBQC4ePEiwsPD4ezsDDs7O/j4+ODbb7mSAjUQNicSmZeNGzdi9OjR2LBhAyIiIhAWFgYHBwccPHgQP/74I+zt7TFw4ECUl5ejd+/eaNeuHf7v//5PdXxFRQU2bNiA8ePHAwBiYmJQVlaGAwcO4Pjx40hJSYG9vb2hbo/IZLE5kczesmXL8N577+Hrr79Gnz59sH79eiiVSvzzn/9ULd+zZs0aNGnSBJmZmRgwYAAmTJiANWvWYNq0aQCAr7/+Gvfu3UNERAQA4NKlSxgxYgS6dOkCAGjXrp1hbo7MkxnNE2MSI7O2ZcsWFBYW4scff0T37t0BAL/99hvOnTsHBwcHtX3v3buHP//8EwDw8ssv4/3338fPP/+MJ598EmvXrkVERATs7OwAAJMnT8Ybb7yB7777DqGhoRgxYgS6du3asDdH5suMntjB5kQya926dUOLFi2wevVq1dLwd+7cQUBAAHJyctS2M2fOYMyYMQAAFxcXhIeHY82aNSgoKMCuXbtUTYkA8Morr+D8+fMYN24cjh8/jsDAQHz88ccGuUciU8aaGJk1b29vLF68GH379oWlpSWWLl2KJ554Aps3b4aLi0uNDz595ZVXMHr0aLRq1Qre3t4ICQlRe9/T0xMTJ07ExIkTkZCQgFWrVuHNN9981LdEBCGUEDoupaLr8Q2FNTEye4899hj279+PL7/8EnFxcRg7diyaN2+OoUOH4uDBg8jNzUVmZiYmT56My5cvq44LCwuDo6Mj5syZg+joaLVzxsXFYc+ePcjNzUV2djb279+Pxx9/vKFvjcyVEA+aA3XZ2CdGJB0dO3bE999/r6qRHThwANOnT8fw4cNx+/ZttGzZEv3791ermVlYWODll1/GvHnzEBkZqXY+hUKBmJgYXL58GY6Ojhg4cCD+8Y9/NPRtEZk8ridGpIMJEybg+vXr2LFjh6FDIVKtJ9bfaRysZNY6neu+KEdG8f9xPTEiU1RcXIzjx49j48aNTGBkfJRKQKZjn5ZE+sSYxIjqYejQocjKysLEiRPxzDPPGDocInVCD0PsJdJIxyRGVA+ZmZmGDoGIwCRGRGRyhFIJoWNzolSG2DOJERGZGjNqTuQ8MSIikizWxIiITI1SADLzqIkxiRERmRohoPOimBJJYmxOJCIiyWJNjIjIxAilgNCxOVEqD3NiTYyIyNQIpX62Olq2bBm8vLxgY2OD4OBgZGVl1bj/v/71L3Tq1Ak2Njbo0qULvv322zpfk0mMiIh0tnnzZsTHxyMpKQnZ2dnw8/NDWFgYCgsLNe7/008/YfTo0ZgwYQJ+/fVXDBs2DMOGDcOJEyfqdF0+AJiIyERUPgC4r+x5WMka6XSu+6ICmWKb1g8ADg4ORvfu3bF06VIAgFKphKenJ958803MmDGjyv4jR45EaWkpdu7cqSp78skn4e/vj/T0dK3jZE2MiMjUNHBzYnl5OY4dO4bQ0FBVmYWFBUJDQ3H48GGNxxw+fFhtf+DBGn3V7V8dDuwgIjIx91Gh8wM77qMCwIPa3cPkcjnkcrlaWVFRERQKBVxdXdXKXV1dcerUKY3nz8/P17h/fn5+neJkEiMiMhHW1tZwc3PDofy6D5DQxN7eHp6enmplSUlJmDVrll7Orw9MYkREJsLGxga5ubkoLy/Xy/mEEJDJZGplf6+FAUDz5s1haWmJgoICtfKCggK4ublpPLebm1ud9q8OkxgRkQmxsbGBjY1Ng17T2toaAQEByMjIwLBhwwA8GNiRkZGB2NhYjcf06NEDGRkZiIuLU5Xt3bsXPXr0qNO1mcSIiEhn8fHxiIqKQmBgIIKCgpCWlobS0lJER0cDACIjI9GyZUskJycDAKZMmYI+ffpg8eLFGDx4MDZt2oSjR49i5cqVdboukxgREels5MiRuH79OmbOnIn8/Hz4+/tj9+7dqsEbly5dgoXF/wbE9+zZExs3bsT777+Pd999Fx06dMD27dvh6+tbp+tynhgREUkW54kREZFkMYkREZFkMYkREZFkMYkREZFkMYkREZFkMYkREZFkMYkREZFkMYkREZFkMYkREZFkMYkREZFkMYkREZFkMYkREZFk/T/Qcz0oCgnhnwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 480x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def generate_square_subsequent_mask(sz: int) -> Tensor:\n",
    "    # The masked positions are filled with True.\n",
    "    # Unmasked positions are filled with False.\n",
    "    # torch.ones(sz, sz): 创建一个全为 1 的 sz × sz 的矩阵。\n",
    "    # torch.triu(...): 使用 triu 函数取得矩阵的上三角部分，将主对角线以下部分置零。\n",
    "    mask = (torch.triu(torch.ones(sz, sz)) == 0).transpose(-1, -2).bool()\n",
    "    return mask\n",
    "\n",
    "\n",
    "#画出一个16×16的矩阵热力图，黄色部分为True，是掩码\n",
    "plt.matshow(generate_square_subsequent_mask(16))\n",
    "plt.colorbar()\n",
    "plt.xlabel(\"keys\")\n",
    "plt.ylabel(\"querys\")\n",
    "plt.title(\"1 means mask while 0 means unmask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2548d49c0f7a8fd5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.199068Z",
     "start_time": "2025-02-06T14:04:30.668599Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T12:20:45.244023Z",
     "iopub.status.busy": "2025-02-07T12:20:45.243647Z",
     "iopub.status.idle": "2025-02-07T12:20:45.728593Z",
     "shell.execute_reply": "2025-02-07T12:20:45.727888Z",
     "shell.execute_reply.started": "2025-02-07T12:20:45.243991Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['[BOS]', '[UNK]', 'quick', 'brown', '[UNK]', 'jumps', 'over', 'the', '[UNK]', 'dog', '.', '[EOS]']\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "['[BOS]', '[UNK]', 'does', 'the', '[UNK]', 'say', '?', '[EOS]', '[PAD]', '[PAD]', '[PAD]', '[PAD]']\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#通过下面代码查看mask的效果\n",
    "inputs_words = [\"The quick brown fox jumps over the lazy dog .\", \"What does the fox say ?\"]\n",
    "\n",
    "inputs_ids, inputs_mask = tokenizer.encode([w.split() for w in inputs_words], return_mask=True)\n",
    "for i in range(len(inputs_ids)):\n",
    "    decode_text = \\\n",
    "        tokenizer.decode(inputs_ids[i:i + 1].tolist(), remove_bos=False, remove_eos=False, remove_pad=False,\n",
    "                         split=True)[0]\n",
    "    print(decode_text)\n",
    "    print(\"-\" * 50)\n",
    "\n",
    "    print(inputs_mask[i].reshape(1, -1))\n",
    "    print(\"-\" * 50)\n",
    "    # reshape(1, -1):将 input_mask[i] 的形状调整为 (1, sequence_length)\n",
    "    # repeat_interleave 在第 0 维（dim=0）上重复 sequence_length 次，生成形状为 (sequence_length, sequence_length) 的掩码。\n",
    "    self_attn_mask = inputs_mask[i].reshape(1, -1).repeat_interleave(inputs_ids.shape[-1], dim=0)\n",
    "    print(self_attn_mask)\n",
    "    print(\"-\" * 50)\n",
    "\n",
    "    look_ahead_mask = generate_square_subsequent_mask(inputs_ids.shape[-1])\n",
    "\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "    axs[0].matshow(self_attn_mask)\n",
    "    axs[0].set_title(\"self_attn_mask\")\n",
    "    axs[0].set_yticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[0].set_ylabel(\"querys\")\n",
    "    axs[0].set_xticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[0].set_xlabel(\"keys\")\n",
    "    axs[1].matshow(look_ahead_mask)\n",
    "    axs[1].set_title(\"look_ahead_mask\")\n",
    "    axs[1].set_yticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[1].set_ylabel(\"querys\")\n",
    "    axs[1].set_xticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[1].set_xlabel(\"keys\")\n",
    "    plt.show()\n",
    "    print('-' * 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14c5b12ee51329e8",
   "metadata": {},
   "source": [
    "## Transformer Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "89a32ab3ca75bf0c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.215535Z",
     "start_time": "2025-02-06T14:04:31.200067Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T12:20:45.729845Z",
     "iopub.status.busy": "2025-02-07T12:20:45.729523Z",
     "iopub.status.idle": "2025-02-07T12:20:45.748796Z",
     "shell.execute_reply": "2025-02-07T12:20:45.747980Z",
     "shell.execute_reply.started": "2025-02-07T12:20:45.729823Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerOutput:\n",
    "    logits: Tensor\n",
    "    encoder_last_hidden_states: Tensor\n",
    "    encoder_attn_scores: List[Tensor]  #画图\n",
    "    decoder_last_hidden_states: Tensor\n",
    "    decoder_self_attn_scores: List[Tensor]  #画图\n",
    "    decoder_cross_attn_scores: List[Tensor]  #画图\n",
    "    preds: Optional[Tensor] = None\n",
    "\n",
    "\n",
    "class TransformerModel(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]\n",
    "        self.num_encoder_layers = config[\"num_encoder_layers\"]\n",
    "        self.num_decoder_layers = config[\"num_decoder_layers\"]\n",
    "        self.pad_idx = config[\"pad_idx\"]\n",
    "        self.bos_idx = config[\"bos_idx\"]\n",
    "        self.eos_idx = config[\"eos_idx\"]\n",
    "        self.vocab_size = config[\"vocab_size\"]\n",
    "        self.dropout_rate = config[\"dropout\"]\n",
    "        self.max_length = config[\"max_length\"]\n",
    "        # 是否共享词嵌入，\n",
    "        # 共享后，decoder的词嵌入层和encoder的词嵌入层相同，节省内存\n",
    "        self.share = config[\"share_embedding\"]\n",
    "\n",
    "        # layers\n",
    "        self.src_embedding = TransformerEmbedding(config)  # 输入的嵌入层\n",
    "        # 如果共享词嵌入，则使用src_embedding作为trg_embedding\n",
    "        if self.share:\n",
    "            # 源和目标的嵌入层相同，共享参数，节省内存\n",
    "            self.trg_embedding = self.src_embedding\n",
    "            self.linear = lambda x: torch.matmul(\n",
    "                # x是该层的输入，[batch_size, seq_len, hidden_size]\n",
    "                # self.trg_embedding.get_word_embedding_weight().T: [hidden_size,vocab_size]\n",
    "                x, self.trg_embedding.get_word_embedding_weight().T\n",
    "            )  # 输出层，共享参数，直接拿原有embedding矩阵的转置，节省内存\n",
    "        else:\n",
    "            self.trg_embedding = TransformerEmbedding(config)  # decoder模块的嵌入层\n",
    "            self.linear = nn.Linear(self.hidden_size, self.vocab_size)  # 输出层\n",
    "\n",
    "        self.encoder = TransformerEncoder(config)\n",
    "        self.decoder = TransformerDecoder(config)\n",
    "\n",
    "        self._init_weights()  # init weights\n",
    "\n",
    "    def _init_weights(self):\n",
    "        # self.parameters(): 这个方法返回模型中的所有可学习参数（权重和偏置）。\n",
    "        for p in self.parameters():\n",
    "            if p.dim() > 1:  # 如果参数是二维或更高维（通常是权重矩阵），则执行初始化。\n",
    "                nn.init.xavier_uniform_(p)  # 使用 xavier 均匀分布来初始化权重\n",
    "\n",
    "    def generate_square_subsequent_mask(self, sz: int) -> Tensor:\n",
    "        # 为了生成斜三角的mask\n",
    "        mask = (torch.triu(torch.ones(sz, sz)) == 0).transpose(-1, -2).bool()\n",
    "        return mask\n",
    "\n",
    "    # 用于训练,在TransformerBlock中的每一层内是并行的，层间是串行的\n",
    "    def forward(\n",
    "            self, encoder_inputs, decoder_inputs, encoder_inputs_mask=None\n",
    "    ) -> TransformerOutput:\n",
    "        # encoder_inputs: [batch_size, src_len]\n",
    "        # decoder_inputs: [batch_size, trg_len]\n",
    "        # encoder_inputs_mask: [batch_size, src_len]\n",
    "        if encoder_inputs_mask is None:\n",
    "            # 返回一个布尔张量，形状与 encoder_inputs 相同。\n",
    "            # 该布尔张量的每个位置会根据 encoder_inputs 中的值是否等于 self.pad_idx 来设置：\n",
    "            # 如果相等，值为 True（表示该位置是填充位置）。\n",
    "            # 如果不相等，值为 False（表示该位置是有效输入） \n",
    "            encoder_inputs_mask = encoder_inputs.eq(self.pad_idx)\n",
    "            # encoder_inputs_mask: [batch_size, src_len]\n",
    "        # unsqueeze(1): 这个操作在张量的第 1 个维度上增加一个新的维度\n",
    "        #    得到 (batch_size, 1, src_len)。\n",
    "        # unsqueeze(2): 这个操作在张量的第 2 个维度上再次增加一个新的维度。\n",
    "        #    得到 (batch_size, 1, 1, src_len)。\n",
    "        encoder_inputs_mask = encoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, src_len],用于encoder的自注意力\n",
    "\n",
    "        look_ahead_mask = self.generate_square_subsequent_mask(decoder_inputs.shape[-1])  # [trg_len, trg_len]\n",
    "        look_ahead_mask = (\n",
    "            look_ahead_mask.unsqueeze(0).unsqueeze(0).to(decoder_inputs.device)\n",
    "        )  # [1, 1, trg_len, trg_len] 用于decoder的自注意力\n",
    "\n",
    "        # 增加decoder_inputs_mask和look_ahead_mask进行组合\n",
    "        decoder_inputs_mask = decoder_inputs.eq(self.pad_idx)\n",
    "        # [batch_size, trg_len]，和上面encoder_inputs_mask一致\n",
    "        decoder_inputs_mask = decoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, trg_len]\n",
    "        decoder_inputs_mask = decoder_inputs_mask + look_ahead_mask\n",
    "        # [batch_size, 1, 1, trg_len]与[1, 1, trg_len, trg_len]相加，得到decoder的自注意力mask\n",
    "        # decoder_inputs_mask : [batch_size, 1, trg_len,trg_len]\n",
    "\n",
    "        # encoding\n",
    "        # src_embedding是TransformerEmbedding(config)\n",
    "        encoder_inputs_embeds = self.src_embedding(encoder_inputs)\n",
    "        # encoder是TransformerEncoder(config)\n",
    "        encoder_outputs = self.encoder(\n",
    "            encoder_inputs_embeds, attn_mask=encoder_inputs_mask\n",
    "        )  # encoder_inputs_mask用于encoder的自注意力,广播去做计算 \n",
    "\n",
    "        # decoding\n",
    "        # decoder_inputs_embeds是TransformerEmbedding(config)\n",
    "        decoder_inputs_embeds = self.trg_embedding(decoder_inputs)\n",
    "        # decoder是TransformerDecoder(config)\n",
    "        decoder_outputs = self.decoder(\n",
    "            decoder_inputs_embeds=decoder_inputs_embeds,\n",
    "            encoder_outputs=encoder_outputs.last_hidden_states,\n",
    "            attn_mask=decoder_inputs_mask,  # 用于decoder的自注意力,广播去做计算\n",
    "            cross_attn_mask=encoder_inputs_mask,  # 用于decoder的交叉注意力,广播去做计算\n",
    "        )\n",
    "        # decoder_outputs.last_hidden_states: [batch_size, trg_len, hidden_size]\n",
    "        logits = self.linear(decoder_outputs.last_hidden_states)\n",
    "        # [batch_size, trg_len, vocab_size]\n",
    "\n",
    "        return TransformerOutput(\n",
    "            logits=logits,\n",
    "            encoder_last_hidden_states=encoder_outputs.last_hidden_states,\n",
    "            encoder_attn_scores=encoder_outputs.attn_scores,\n",
    "            decoder_last_hidden_states=decoder_outputs.last_hidden_states,\n",
    "            decoder_self_attn_scores=decoder_outputs.self_attn_scores,\n",
    "            decoder_cross_attn_scores=decoder_outputs.cross_attn_scores,\n",
    "        )\n",
    "\n",
    "    @torch.no_grad()  # 禁用梯度计算\n",
    "    def infer(self, encoder_inputs, encoder_inputs_mask=None) -> TransformerOutput:\n",
    "        # 应对多个样本同时进行推理\n",
    "        if encoder_inputs_mask is None:\n",
    "            encoder_inputs_mask = encoder_inputs.eq(self.pad_idx)\n",
    "            # encoder_inputs_mask: [batch_size, src_len]\n",
    "        encoder_inputs_mask = encoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, src_len],用于encoder的自注意力\n",
    "\n",
    "        look_ahead_mask = self.generate_square_subsequent_mask(self.max_length)\n",
    "        # [max_length, max_length]\n",
    "        look_ahead_mask = (\n",
    "            look_ahead_mask.unsqueeze(0).unsqueeze(0).to(encoder_inputs.device)\n",
    "        )  # [1, 1, max_length, max_length] 用于decoder的自注意力\n",
    "\n",
    "        # encoding\n",
    "        # src_embedding是TransformerEmbedding(config)\n",
    "        encoder_inputs_embeds = self.src_embedding(encoder_inputs)\n",
    "        # encoder是TransformerEncoder(config)\n",
    "        # 因为只支持单样本预测，没有paddings，所以不需要mask\n",
    "        encoder_outputs = self.encoder(encoder_inputs_embeds)\n",
    "        # encoder_outputs.last_hidden_states: [batch_size, src_len, hidden_size]\n",
    "\n",
    "        # decoding,多样本推理\n",
    "        # encoder_inputs.shape[0]: 获取编码器输入的批量大小（batch size），即样本的数量。\n",
    "        # .reshape(-1, 1): 这个操作将张量的形状调整为 (batch_size, 1)，\n",
    "        decoder_inputs = torch.Tensor([self.bos_idx] * encoder_inputs.shape[0]).reshape(-1, 1).long().to(\n",
    "            device=encoder_inputs.device)\n",
    "        # 推理是串行的，每次只推理一个token，所以需要初始化decoder_inputs为bos_idx\n",
    "        for cur_len in tqdm(range(1, self.max_length + 1)):\n",
    "            decoder_inputs_embeds = self.trg_embedding(decoder_inputs)\n",
    "            decoder_outputs = self.decoder(\n",
    "                decoder_inputs_embeds=decoder_inputs_embeds,\n",
    "                encoder_outputs=encoder_outputs.last_hidden_states,\n",
    "                # decoder的自注意力mask\n",
    "                # 因为是串行的，所以每次只看前面cur_len个token\n",
    "                attn_mask=look_ahead_mask[:, :, :cur_len, :cur_len],\n",
    "            )\n",
    "            # decoder_outputs.last_hidden_states: [batch_size, cur_len, hidden_size]\n",
    "            logits = self.linear(decoder_outputs.last_hidden_states)\n",
    "            # logits: [batch_size, cur_len, vocab_size]\n",
    "            # 通过最大下标确定类别，[:, -1:]表示取最后一个结果\n",
    "            next_token = logits.argmax(dim=-1)[:, -1:]  # [batch_size, 1]\n",
    "            # 预测输出拼接到输入中\n",
    "            decoder_inputs = torch.cat([decoder_inputs, next_token], dim=-1)\n",
    "            # dcoder_inputs: [batch_size, cur_len+1]\n",
    "\n",
    "            # (decoder_inputs == self.eos_idx).sum(dim=-1)是判断样本中是否含有EOS标记\n",
    "            # all是每一个都为True，才会结束\n",
    "            if all((decoder_inputs == self.eos_idx).sum(dim=-1) > 0):\n",
    "                break\n",
    "\n",
    "        return TransformerOutput(\n",
    "            preds=decoder_inputs[:, 1:],  # 去掉bos_idx\n",
    "            logits=logits,\n",
    "            encoder_last_hidden_states=encoder_outputs.last_hidden_states,\n",
    "            encoder_attn_scores=encoder_outputs.attn_scores,\n",
    "            decoder_last_hidden_states=decoder_outputs.last_hidden_states,\n",
    "            decoder_self_attn_scores=decoder_outputs.self_attn_scores,\n",
    "            decoder_cross_attn_scores=decoder_outputs.cross_attn_scores,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a740def7afa83f9",
   "metadata": {},
   "source": [
    "# 训练"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18175b791877293",
   "metadata": {},
   "source": [
    "## 损失函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a2b1b4622966eb25",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.222855Z",
     "start_time": "2025-02-06T14:04:31.217548Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T12:20:45.750634Z",
     "iopub.status.busy": "2025-02-07T12:20:45.749802Z",
     "iopub.status.idle": "2025-02-07T12:20:45.756467Z",
     "shell.execute_reply": "2025-02-07T12:20:45.755652Z",
     "shell.execute_reply.started": "2025-02-07T12:20:45.750598Z"
    }
   },
   "outputs": [],
   "source": [
    "class CrossEntropyWithPadding:\n",
    "    def __init__(self, config):\n",
    "        self.label_smoothing = config[\"label_smoothing\"]\n",
    "\n",
    "    def __call__(self, logits, labels, padding_mask=None):\n",
    "        # logits: [batch_size, seq_len, vocab_size]\n",
    "        # labels: [batch_size, seq_len]\n",
    "        # padding_mask: [batch_size, seq_len]\n",
    "        bs, seq_len, nc = logits.shape\n",
    "        loss = F.cross_entropy(\n",
    "            logits.reshape(bs * seq_len, nc),\n",
    "            labels.reshape(-1),\n",
    "            reduction='none',  # 不对损失进行归约（reduction\n",
    "            label_smoothing=self.label_smoothing\n",
    "        )  # label_smoothing表示随机将一个类别的概率设置为0.1，使得模型更加关注其他类别\n",
    "        # loss: [batch_size * seq_len, 1]\n",
    "\n",
    "        if padding_mask is None:\n",
    "            loss = loss.mean()  # 计算平均损失\n",
    "        else:\n",
    "            # 将padding_mask reshape成一维张量，mask部分为0，非mask部分为1\n",
    "            padding_mask = 1 - padding_mask.reshape(-1)\n",
    "            loss = torch.mul(loss, padding_mask).sum() / padding_mask.sum()\n",
    "\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab581173629b9cc",
   "metadata": {},
   "source": [
    "## 学习率衰减"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "39efc556d490f34e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.228130Z",
     "start_time": "2025-02-06T14:04:31.223857Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T12:20:45.757649Z",
     "iopub.status.busy": "2025-02-07T12:20:45.757355Z",
     "iopub.status.idle": "2025-02-07T12:20:45.762514Z",
     "shell.execute_reply": "2025-02-07T12:20:45.761749Z",
     "shell.execute_reply.started": "2025-02-07T12:20:45.757627Z"
    }
   },
   "outputs": [],
   "source": [
    "# NoamDecayScheduler 是一个自定义或外部定义的学习率衰减调度器类。\n",
    "# 它需要接收配置 config 作为参数，可能实现了特定的学习率衰减方案\n",
    "class NoamDecayScheduler:\n",
    "    def __init__(self, config):\n",
    "        self.d_model = config[\"d_model\"]\n",
    "        self.warmup_steps = config[\"warmup_steps\"]\n",
    "\n",
    "    # __call__ 方法是一个特殊的方法，用于使对象能够像函数一样被调用\n",
    "    def __call__(self, step):\n",
    "        step += 1\n",
    "        arg1 = step ** (-0.5)  # 4000步之后是arg1\n",
    "        arg2 = step * (self.warmup_steps ** (-1.5))  # 4000步之前是arg2\n",
    "        arg3 = self.d_model ** (-0.5)\n",
    "\n",
    "        return arg3 * np.minimum(arg1, arg2)  # minimum是取两个数中的较小值\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3188d631aaa09057",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.334627Z",
     "start_time": "2025-02-06T14:04:31.229133Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T12:20:45.763848Z",
     "iopub.status.busy": "2025-02-07T12:20:45.763375Z",
     "iopub.status.idle": "2025-02-07T12:20:45.881850Z",
     "shell.execute_reply": "2025-02-07T12:20:45.881375Z",
     "shell.execute_reply.started": "2025-02-07T12:20:45.763821Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_learning_rate_schedule = NoamDecayScheduler({\"d_model\": 512, \"warmup_steps\": 4000})\n",
    "#下面是学习率的设计图\n",
    "plt.plot(temp_learning_rate_schedule(np.arange(0, 40000)))\n",
    "plt.ylabel(\"Leraning rate\")\n",
    "plt.xlabel(\"Train step\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90185dd5b91d52ad",
   "metadata": {},
   "source": [
    "## 优化器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a5061e4cd6464c1b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.339984Z",
     "start_time": "2025-02-06T14:04:31.335629Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T12:20:45.882662Z",
     "iopub.status.busy": "2025-02-07T12:20:45.882441Z",
     "iopub.status.idle": "2025-02-07T12:20:45.886557Z",
     "shell.execute_reply": "2025-02-07T12:20:45.886069Z",
     "shell.execute_reply.started": "2025-02-07T12:20:45.882644Z"
    }
   },
   "outputs": [],
   "source": [
    "from torch.optim.lr_scheduler import LambdaLR\n",
    "from torch.optim import Adam\n",
    "\n",
    "\n",
    "def get_optimizer(model, config):\n",
    "    base_lr = 0.1\n",
    "    beta1 = config[\"beta1\"]  # Adam 的 beta1\n",
    "    beta2 = config[\"beta2\"]  # Adam 的 beta2\n",
    "    eps = config[\"eps\"]\n",
    "    optimizer = Adam(model.parameters(), lr=base_lr, betas=(beta1, beta2), eps=eps)\n",
    "    # config是一个字典，包含了学习率衰减的参数\n",
    "    lr_scheduler = NoamDecayScheduler(config)\n",
    "    # 使用 LambdaLR 调度器，它可以根据给定的函数 lr_lambda 调整学习率。\n",
    "    # 这里将 lr_scheduler 作为函数传递给 LambdaLR，它包含了特定于模型或任务的学习率调度规则\n",
    "    scheduler = LambdaLR(optimizer, lr_lambda=lr_scheduler)\n",
    "    return optimizer, scheduler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "49a2f5433a409089",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.346703Z",
     "start_time": "2025-02-06T14:04:31.340986Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T12:20:45.887413Z",
     "iopub.status.busy": "2025-02-07T12:20:45.887193Z",
     "iopub.status.idle": "2025-02-07T12:20:45.893651Z",
     "shell.execute_reply": "2025-02-07T12:20:45.893115Z",
     "shell.execute_reply.started": "2025-02-07T12:20:45.887394Z"
    }
   },
   "outputs": [],
   "source": [
    "class SaveCheckpointsCallback:\n",
    "    def __init__(self, save_dir, save_step=5000, save_best_only=True):\n",
    "        self.save_dir = save_dir  # 保存路径\n",
    "        self.save_step = save_step  # 保存步数\n",
    "        self.save_best_only = save_best_only  # 是否只保存最好的模型\n",
    "        self.best_metric = -np.inf  # 最好的指标，指标不可能为负数，所以初始化为-1\n",
    "        # 创建保存路径\n",
    "        if not os.path.exists(self.save_dir):  # 如果不存在保存路径，则创建\n",
    "            os.makedirs(self.save_dir)\n",
    "\n",
    "    # 对象被调用时：当你将对象像函数一样调用时，Python 会自动调用 __call__ 方法。\n",
    "    # state_dict() 返回模型参数的字典，包括模型参数和优化器参数\n",
    "    # metric 是指标，可以是验证集的准确率，也可以是其他指标\n",
    "    def __call__(self, step, state_dict, metric=None):\n",
    "        if step % self.save_step > 0:\n",
    "            return  # 不是保存步数，则直接返回\n",
    "\n",
    "        if self.save_best_only:\n",
    "            assert metric is not None  # 必须传入metric\n",
    "            if metric >= self.best_metric:  # 如果当前指标大于最好的指标\n",
    "                # save checkpoint\n",
    "                # 保存最好的模型，覆盖之前的模型，不保存step，只保存state_dict，即模型参数，不保存优化器参数\n",
    "                torch.save(state_dict, os.path.join(self.save_dir, \"nums_layers_8.ckpt\"))\n",
    "                self.best_metric = metric  # 更新最好的指标\n",
    "        else:\n",
    "            # 保存模型\n",
    "            torch.save(state_dict, os.path.join(self.save_dir, f\"{step}.ckpt\"))\n",
    "            # 保存每个step的模型，不覆盖之前的模型，保存step，保存state_dict，即模型参数，不保存优化器参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1c98380e43240291",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.352748Z",
     "start_time": "2025-02-06T14:04:31.347708Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T12:20:45.894636Z",
     "iopub.status.busy": "2025-02-07T12:20:45.894321Z",
     "iopub.status.idle": "2025-02-07T12:20:45.898531Z",
     "shell.execute_reply": "2025-02-07T12:20:45.898032Z",
     "shell.execute_reply.started": "2025-02-07T12:20:45.894619Z"
    }
   },
   "outputs": [],
   "source": [
    "class EarlyStopCallback:\n",
    "    def __init__(self, patience=5, min_delta=0.01):\n",
    "        self.patience = patience  # 多少个step没有提升就停止训练\n",
    "        self.min_delta = min_delta  # 最小的提升幅度\n",
    "        self.best_metric = -np.inf  # 记录的最好的指标\n",
    "        self.counter = 0  # 计数器，记录连续多少个step没有提升\n",
    "\n",
    "    def __call__(self, metric):\n",
    "        if metric >= self.best_metric + self.min_delta:  # 如果指标提升了\n",
    "            self.best_metric = metric  # 更新最好的指标\n",
    "            self.counter = 0  # 计数器清零\n",
    "        else:\n",
    "            self.counter += 1  # 计数器加一\n",
    "\n",
    "    @property  # 使用@property装饰器，使得 对象.early_stop可以调用，不需要()\n",
    "    def early_stop(self):\n",
    "        # 如果计数器大于等于patience，则返回True，停止训练\n",
    "        return self.counter >= self.patience"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebb3c8c230d312c1",
   "metadata": {},
   "source": [
    "## training & valuating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "140f2acfbeb9740a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.358185Z",
     "start_time": "2025-02-06T14:04:31.353752Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T12:20:45.899420Z",
     "iopub.status.busy": "2025-02-07T12:20:45.899190Z",
     "iopub.status.idle": "2025-02-07T12:20:45.903713Z",
     "shell.execute_reply": "2025-02-07T12:20:45.903126Z",
     "shell.execute_reply.started": "2025-02-07T12:20:45.899402Z"
    }
   },
   "outputs": [],
   "source": [
    "@torch.no_grad()  # 禁用梯度计算\n",
    "def evaluating(model, data_loader, loss_fct):\n",
    "    loss_list = []\n",
    "    for batch in data_loader:\n",
    "        encoder_inputs = batch[\"encoder_inputs\"]\n",
    "        encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "        decoder_inputs = batch[\"decoder_inputs\"]\n",
    "        decoder_labels = batch[\"decoder_labels\"]\n",
    "        decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "\n",
    "        # 前向计算\n",
    "        outputs = model(\n",
    "            encoder_inputs=encoder_inputs,\n",
    "            decoder_inputs=decoder_inputs,\n",
    "            encoder_inputs_mask=encoder_inputs_mask,\n",
    "        )\n",
    "        logits = outputs.logits\n",
    "        # 计算损失\n",
    "        loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)\n",
    "        loss_list.append(loss.cpu().item())\n",
    "\n",
    "    return np.mean(loss_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3099e48677dcb6bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.366317Z",
     "start_time": "2025-02-06T14:04:31.359188Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T12:20:45.904747Z",
     "iopub.status.busy": "2025-02-07T12:20:45.904352Z",
     "iopub.status.idle": "2025-02-07T12:20:45.912425Z",
     "shell.execute_reply": "2025-02-07T12:20:45.911842Z",
     "shell.execute_reply.started": "2025-02-07T12:20:45.904728Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def training(model,\n",
    "             train_loader,\n",
    "             val_loader,\n",
    "             epoch,\n",
    "             loss_fct,\n",
    "             optimizer,\n",
    "             scheduler=None,\n",
    "             save_ckpt_callback=None,\n",
    "             early_stop_callback=None,\n",
    "             eval_step=500,\n",
    "             ):\n",
    "    record_dict = {\n",
    "        \"train\": [],\n",
    "        \"val\": []\n",
    "    }\n",
    "\n",
    "    global_step = 1  # 全局步数\n",
    "    model.train()  # 训练模式\n",
    "    with tqdm(total=epoch * len(train_loader)) as pbar:\n",
    "        for epoch_id in range(epoch):\n",
    "            # 训练\n",
    "            for batch in train_loader:\n",
    "                encoder_inputs = batch[\"encoder_inputs\"]\n",
    "                encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "                decoder_inputs = batch[\"decoder_inputs\"]\n",
    "                decoder_labels = batch[\"decoder_labels\"]\n",
    "                decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "\n",
    "                optimizer.zero_grad()  # 梯度清零\n",
    "\n",
    "                # 前向传播\n",
    "                outputs = model(\n",
    "                    encoder_inputs=encoder_inputs,\n",
    "                    decoder_inputs=decoder_inputs,\n",
    "                    encoder_inputs_mask=encoder_inputs_mask\n",
    "                )\n",
    "                logits = outputs.logits\n",
    "                loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)\n",
    "\n",
    "                # 梯度回传\n",
    "                loss.backward()\n",
    "\n",
    "                # 调整优化器，包括学习率的变动等\n",
    "                optimizer.step()\n",
    "                if scheduler is not None:\n",
    "                    scheduler.step()  # 更新学习率\n",
    "\n",
    "                loss = loss.cpu().item()\n",
    "\n",
    "                record_dict[\"train\"].append({\n",
    "                    \"loss\": loss,\n",
    "                    \"step\": global_step\n",
    "                })\n",
    "\n",
    "                # 评估\n",
    "                if global_step % eval_step == 0:\n",
    "                    model.eval()  # 评估模式\n",
    "                    # 验证集损失和准确率\n",
    "                    val_loss = evaluating(model, val_loader, loss_fct)\n",
    "                    record_dict[\"val\"].append({\n",
    "                        \"loss\": val_loss,\n",
    "                        \"step\": global_step\n",
    "                    })\n",
    "                    model.train()  # 训练模式\n",
    "\n",
    "                    # 2. 保存模型权重 save model checkpoint\n",
    "                    if save_ckpt_callback is not None:\n",
    "                        # model.state_dict() 返回模型参数的字典，包括模型参数和优化器参数\n",
    "                        save_ckpt_callback(global_step, model.state_dict(), -val_loss)\n",
    "                        # 保存最好的模型，覆盖之前的模型，保存step，保存state_dict,通过metric判断是否保存最好的模型\n",
    "\n",
    "                    # 3. 早停 early stopping\n",
    "                    if early_stop_callback is not None:\n",
    "                        # 验证集准确率不再提升，则停止训练\n",
    "                        early_stop_callback(-val_loss)\n",
    "                        # 验证集准确率不再提升，则停止训练\n",
    "                        if early_stop_callback.early_stop:\n",
    "                            print(f\"Early stop at epoch {epoch_id} / global_step {global_step}\")\n",
    "                            return record_dict  # 早停，返回记录字典 record_dict\n",
    "\n",
    "                # 更新进度条和全局步数\n",
    "                pbar.update(1)  # 更新进度条\n",
    "                global_step += 1  # 全局步数加一\n",
    "            pbar.set_postfix({\"epoch\": epoch_id, \"loss\": loss, \"val_loss\": val_loss})\n",
    "\n",
    "    return record_dict  # 训练结束，返回记录字典 record_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cf04ac5e18afea4f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.441762Z",
     "start_time": "2025-02-06T14:04:31.367320Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T12:20:45.913359Z",
     "iopub.status.busy": "2025-02-07T12:20:45.913116Z",
     "iopub.status.idle": "2025-02-07T12:20:46.224077Z",
     "shell.execute_reply": "2025-02-07T12:20:46.223496Z",
     "shell.execute_reply.started": "2025-02-07T12:20:45.913341Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load train dataset from wmt16/.cache/de2en_train_128.npy\n",
      "load val dataset from wmt16/.cache/de2en_val_128.npy\n"
     ]
    }
   ],
   "source": [
    "#模型的超参\n",
    "config = {\n",
    "    \"bos_idx\": 1,\n",
    "    \"eos_idx\": 3,\n",
    "    \"pad_idx\": 0,\n",
    "    \"vocab_size\": len(word2idx),\n",
    "    \"max_length\": 128,\n",
    "    \"d_model\": 512, # 可调整\n",
    "    \"dim_feedforward\": 2048,  # FFN 的隐藏层大小\n",
    "    \"dropout\": 0.1, # 可调整\n",
    "    \"layer_norm_eps\": 1e-6,  # 层归一化的 epsilon, 防止除零错误\n",
    "    \"num_heads\": 8, # 可调整\n",
    "    \"num_decoder_layers\": 8, # 可调整\n",
    "    \"num_encoder_layers\": 8, # 可调整\n",
    "    \"label_smoothing\": 0.1,\n",
    "    \"beta1\": 0.9,  # Adam 的 beta1\n",
    "    \"beta2\": 0.98,  # Adam 的 beta2\n",
    "    \"eps\": 1e-9,\n",
    "    \"warmup_steps\": 4_000,\n",
    "    \"share_embedding\": False,  # 是否共享词向量\n",
    "}\n",
    "\n",
    "\n",
    "def get_dl(dataset, batch_size, shuffle=True):\n",
    "    sampler = TransformerBatchSampler(dataset, batch_size=batch_size, shuffle_batch=shuffle)\n",
    "    sample_dl = DataLoader(dataset, batch_sampler=sampler, collate_fn=partial(collate_fn, tokenizer=tokenizer))\n",
    "    return sample_dl\n",
    "\n",
    "\n",
    "# dataset\n",
    "train_ds = LangPairDataset(\"train\", max_length=config[\"max_length\"])\n",
    "val_ds = LangPairDataset(\"val\", max_length=config[\"max_length\"])\n",
    "\n",
    "# tokenizer\n",
    "tokenizer = Tokenizer(word2idx=word2idx, idx2word=idx2word, max_length=config[\"max_length\"])\n",
    "\n",
    "# dataloader\n",
    "batch_size = 2048\n",
    "train_dl = get_dl(train_ds, batch_size=batch_size, shuffle=True)\n",
    "val_dl = get_dl(val_ds, batch_size=batch_size, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c4bd1cb45f5032ee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:32.221751Z",
     "start_time": "2025-02-06T14:04:31.442764Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T12:20:46.225051Z",
     "iopub.status.busy": "2025-02-07T12:20:46.224782Z",
     "iopub.status.idle": "2025-02-07T12:20:47.223252Z",
     "shell.execute_reply": "2025-02-07T12:20:47.222688Z",
     "shell.execute_reply.started": "2025-02-07T12:20:46.225034Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型参数量: 86638783\n"
     ]
    }
   ],
   "source": [
    "#计算模型参数量\n",
    "model = TransformerModel(config)\n",
    "print(f\"模型参数量: {sum(p.numel() for p in model.parameters() if p.requires_grad)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6b87e4917b9ef89b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:53.810386Z",
     "start_time": "2025-02-06T14:04:32.224757Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T12:20:47.224248Z",
     "iopub.status.busy": "2025-02-07T12:20:47.223959Z",
     "iopub.status.idle": "2025-02-07T13:03:11.108023Z",
     "shell.execute_reply": "2025-02-07T13:03:11.107425Z",
     "shell.execute_reply.started": "2025-02-07T12:20:47.224229Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "41499it [42:21, 16.33it/s, epoch=38, loss=2.32, val_loss=3.54]                        "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stop at epoch 39 / global_step 41500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "epoch = 100\n",
    "\n",
    "# model\n",
    "model = TransformerModel(config)\n",
    "model = model.to(device)\n",
    "\n",
    "# 1. 定义损失函数 采用交叉熵损失\n",
    "loss_fct = CrossEntropyWithPadding(config)\n",
    "\n",
    "# 2. 定义优化器\n",
    "optimizer, scheduler = get_optimizer(model, config)\n",
    "\n",
    "# 3.save model checkpoint\n",
    "if not os.path.exists(\"checkpoints\"):\n",
    "    os.makedirs(\"checkpoints\")\n",
    "save_ckpt_callback = SaveCheckpointsCallback(save_dir=\"checkpoints\", save_step=500, save_best_only=True)\n",
    "\n",
    "# 4. early stopping\n",
    "early_stop_callback = EarlyStopCallback(patience=10,min_delta=0.001)\n",
    "\n",
    "record_dict = training(\n",
    "    model,\n",
    "    train_dl,\n",
    "    val_dl,\n",
    "    epoch,\n",
    "    loss_fct,\n",
    "    optimizer,\n",
    "    scheduler,\n",
    "    save_ckpt_callback=save_ckpt_callback,\n",
    "    early_stop_callback=early_stop_callback,\n",
    "    eval_step=500\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50ac7e66d5df05c3",
   "metadata": {},
   "source": [
    "# 推理\n",
    "\n",
    "- 翻译项目的评估指标一般是BLEU4\n",
    "- 接下来进行翻译推理，并作出注意力的热度图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "305c0410623a9f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T13:03:11.109797Z",
     "iopub.status.busy": "2025-02-07T13:03:11.109384Z",
     "iopub.status.idle": "2025-02-07T13:03:11.359390Z",
     "shell.execute_reply": "2025-02-07T13:03:11.358866Z",
     "shell.execute_reply.started": "2025-02-07T13:03:11.109775Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "# 加载模型\n",
    "state_dict = torch.load(f\"checkpoints/nums_layers_8.ckpt\", map_location=\"cpu\",weights_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6f512e7c8f2db972",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T13:03:11.360225Z",
     "iopub.status.busy": "2025-02-07T13:03:11.360031Z",
     "iopub.status.idle": "2025-02-07T13:03:31.572388Z",
     "shell.execute_reply": "2025-02-07T13:03:31.571777Z",
     "shell.execute_reply.started": "2025-02-07T13:03:11.360208Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load test dataset from wmt16/.cache/de2en_test_128.npy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 4-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "6it [00:00, 51.08it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 3-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "24it [00:00, 54.23it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 2-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "966it [00:18, 52.01it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testing loss: 3.4092287785271433\n",
      "testing bleu: 0.5698942517373464\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from nltk.translate.bleu_score import sentence_bleu\n",
    "\n",
    "model = TransformerModel(config)\n",
    "model.load_state_dict(state_dict)\n",
    "\n",
    "loss_fct = CrossEntropyWithPadding(config)\n",
    "\n",
    "test_ds = LangPairDataset(\"test\", max_length=config[\"max_length\"])\n",
    "test_dl = DataLoader(\n",
    "    test_ds, batch_size=1,\n",
    "    collate_fn=partial(collate_fn, tokenizer=tokenizer)\n",
    ")\n",
    "\n",
    "model = model.to(device)\n",
    "model.eval()\n",
    "collect = {}\n",
    "loss_collect = []\n",
    "\n",
    "predictions = []\n",
    "answers = []\n",
    "\n",
    "# 初始化BLEU分数列表\n",
    "bleu_scores = []\n",
    "for idx, batch in tqdm(enumerate(test_dl)):\n",
    "    encoder_inputs = batch[\"encoder_inputs\"]\n",
    "    encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "    decoder_inputs = batch[\"decoder_inputs\"]\n",
    "    decoder_labels = batch[\"decoder_labels\"]\n",
    "\n",
    "    # 前向计算\n",
    "    outputs = model(\n",
    "        encoder_inputs=encoder_inputs,\n",
    "        decoder_inputs=decoder_inputs,\n",
    "        encoder_inputs_mask=encoder_inputs_mask\n",
    "    )\n",
    "\n",
    "    loss = loss_fct(outputs.logits, decoder_labels)  # 验证集损失\n",
    "\n",
    "    preds = outputs.logits.argmax(dim=-1)  # 预测结果，[1,seq_len]\n",
    "    # 把preds转为英文单词\n",
    "    preds = tokenizer.decode(preds.cpu().numpy())  #['预测句子']\n",
    "    #把decoder_labels转为英文单词\n",
    "    decoder_labels = tokenizer.decode(decoder_labels.cpu().numpy())  #['标签句子']\n",
    "\n",
    "    answers.append(decoder_labels)\n",
    "\n",
    "    belu = sentence_bleu([decoder_labels[0].split()], preds[0].split(), weights=(1, 0, 0, 0))\n",
    "    bleu_scores.append(belu)\n",
    "    collect[idx] = {\n",
    "        \"loss\": loss.item(),\n",
    "        \"src_inputs\": encoder_inputs,\n",
    "        \"trg_inputs\": decoder_inputs,\n",
    "        \"mask\": encoder_inputs_mask,\n",
    "        \"trg_labels\": decoder_labels,\n",
    "        \"preds\": preds\n",
    "    }\n",
    "    loss_collect.append(loss.item())\n",
    "\n",
    "# sort collect by value\n",
    "collect = sorted(collect.items(), key=lambda x: x[1][\"loss\"])\n",
    "print(f\"testing loss: {np.array(loss_collect).mean()}\")\n",
    "belu_scores = sum(bleu_scores) / len(bleu_scores)\n",
    "print(f\"testing bleu: {belu_scores}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a9ab2039d24b72a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T13:03:31.573658Z",
     "iopub.status.busy": "2025-02-07T13:03:31.573145Z",
     "iopub.status.idle": "2025-02-07T13:03:31.576688Z",
     "shell.execute_reply": "2025-02-07T13:03:31.576061Z",
     "shell.execute_reply.started": "2025-02-07T13:03:31.573629Z"
    }
   },
   "outputs": [],
   "source": [
    "# !pip install Cython  # if failed to install fastBPE, try this line\n",
    "# !pip install fastBPE -v\n",
    "# 在 Windows 系统上并没有 sys/mman.h 文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1e7c1e0999eb365d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T13:03:31.577952Z",
     "iopub.status.busy": "2025-02-07T13:03:31.577467Z",
     "iopub.status.idle": "2025-02-07T13:03:32.076142Z",
     "shell.execute_reply": "2025-02-07T13:03:32.075397Z",
     "shell.execute_reply.started": "2025-02-07T13:03:31.577929Z"
    }
   },
   "outputs": [],
   "source": [
    "import re\n",
    "from fastBPE import fastBPE\n",
    "from sacremoses import MosesDetokenizer, MosesTokenizer\n",
    "\n",
    "# `MosesTokenizer` 和 `MosesDetokenizer` 是来自 `sacremoses` 库的工具，用于自然语言处理中的分词（Tokenization）和去标记化（Detokenization）。\n",
    "# 这些工具主要用于对文本进行预处理和后处理，通常在处理自然语言处理任务时会用到。\n",
    "\n",
    "# 这些工具通常在文本预处理和后处理过程中使用，对输入的文本进行标记化和去标记化，是一种常用的处理方式。\n",
    "# 在自然语言处理任务中，对文本进行正确的分词和还原是很重要的，而 `MosesTokenizer` 和 `MosesDetokenizer` 提供了方便、高效的工具来处理这些任务。\n",
    "\n",
    "class Translator:\n",
    "    def __init__(self, model, src_tokenizer, trg_tokenizer):\n",
    "        self.bpe = fastBPE(\"./wmt16/bpe.20000\", \"./wmt16/vocab\")\n",
    "        self.mose_tokenizer = MosesTokenizer(lang=\"de\")\n",
    "        self.mose_detokenizer = MosesDetokenizer(lang=\"en\")\n",
    "        self.model = model\n",
    "        self.model.eval()\n",
    "        self.src_tokenizer = src_tokenizer\n",
    "        self.trg_tokenizer = trg_tokenizer\n",
    "        # 匹配出现的 @@ （后面带空格的情况）。\n",
    "        # 或者匹配以 @@ 结尾的情况（可选空格）。\n",
    "        self.pattern = re.compile(r'(@@ )|(@@ ?$)')\n",
    "        \n",
    "    def draw_attention_map(self, attn_scores, cross_attn_scores, src_words_list, trg_words_list):\n",
    "        \"\"\"绘制注意力热力图\n",
    "        attn_scores (numpy.ndarray): 表示自注意力机制（self-attention）分数。\n",
    "        cross_attn_scores (numpy.ndarray): 表示交叉注意力机制的注意力分数。\n",
    "        src_words_list (list): 源语言句子的单词列表。\n",
    "        trg_words_list (list): 目标语言句子的单词列表。\n",
    "        \"\"\"\n",
    "        assert len(attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, target sequence length], but got {attn_scores.shape}\"\n",
    "        attn_scores = attn_scores[:, :len(trg_words_list), :len(trg_words_list)]\n",
    "\n",
    "        assert len(cross_attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, source sequence length], but got {cross_attn_scores.shape}\"\n",
    "        cross_attn_scores = cross_attn_scores[:, :len(trg_words_list), :len(src_words_list)]\n",
    "\n",
    "        num_heads, trg_len, src_len = cross_attn_scores.shape\n",
    "\n",
    "        fig = plt.figure(figsize=(10, 5), constrained_layout=True) # constrained_layout=True 自动调整子图参数，使之填充整个图像区域\n",
    "        grid = plt.GridSpec(trg_len, trg_len + src_len, wspace=0.1, hspace=0.1)# wspace,hspace 控制子图之间的间距\n",
    "        #下面是attn_scores的热力图\n",
    "        self_map = fig.add_subplot(grid[:,:trg_len]) #  添加子图\n",
    "        self_map.matshow(attn_scores.mean(dim=0), cmap='viridis') # 绘制热力图，cmap表示颜色,dim=0表示对第0维求均值\n",
    "        self_map.set_yticks(range(trg_len), trg_words_list, fontsize=10)\n",
    "        self_map.set_xticks(range(trg_len), [\"[BOS]\"] + trg_words_list[:-1], rotation=90)\n",
    "        #下面是cross_attn_scores的热力图\n",
    "        cross_map = fig.add_subplot(grid[:, trg_len:])\n",
    "        cross_map.matshow(cross_attn_scores.mean(dim=0), cmap='viridis')\n",
    "        cross_map.set_yticks(range(trg_len), [], fontsize=6)\n",
    "        cross_map.set_xticks(range(src_len), src_words_list, rotation=90)\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "    def draw_attention_maps(self, attn_scores, cross_attn_scores, src_words_list, trg_words_list, heads_list):\n",
    "        \"\"\"绘制注意力热力图\n",
    "\n",
    "        Args:\n",
    "            - scores (numpy.ndarray): shape = [source sequence length, target sequence length]\n",
    "        \"\"\"\n",
    "        assert len(attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, target sequence length], but got {attn_scores.shape}\"\n",
    "        attn_scores = attn_scores[:, :len(trg_words_list), :len(trg_words_list)]\n",
    "\n",
    "        assert len(cross_attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, source sequence length], but got {cross_attn_scores.shape}\"\n",
    "        cross_attn_scores = cross_attn_scores[:, :len(trg_words_list), :len(src_words_list)]\n",
    "        # cross_attn_scores = cross_attn_scores[:, :len(src_words_list), :len(src_words_list)]\n",
    "\n",
    "        num_heads, trg_len, src_len = cross_attn_scores.shape\n",
    "        fig, axes = plt.subplots(2, len(heads_list), figsize=(5 * len(heads_list), 10))\n",
    "        for i, heads_idx in enumerate(heads_list):\n",
    "            axes[0, i].matshow(attn_scores[heads_idx], cmap='viridis')\n",
    "            axes[0, i].set_yticks(range(trg_len), trg_words_list)\n",
    "            axes[0, i].set_xticks(range(trg_len), [\"[BOS]\"] + trg_words_list[:-1], rotation=90)\n",
    "            axes[0, i].set_title(f\"head {heads_idx}\")\n",
    "            axes[1, i].matshow(cross_attn_scores[heads_idx], cmap='viridis')\n",
    "            axes[1, i].set_yticks(range(trg_len), trg_words_list)\n",
    "            axes[1, i].set_xticks(range(src_len), src_words_list, rotation=90)\n",
    "            axes[1, i].set_title(f\"head {heads_idx}\")\n",
    "\n",
    "        plt.show()\n",
    "    \n",
    "    def __call__(self, sentence_list,heads_list=None,layer_idx=-1):\n",
    "        # 将输入句子列表转换为小写，并使用 MosesTokenizer 进行分词处理。\n",
    "        sentence_list = [\" \".join(self.mose_tokenizer.tokenize(s.lower())) for s in sentence_list]\n",
    "        # 将分词后的结果进行 BPE 编码，得到 tokens_list。\n",
    "        tokens_list = [s.split() for s in self.bpe.apply(sentence_list)]\n",
    "        \n",
    "        # 使用 src_tokenizer 对 tokens_list 进行编码，同时添加起始标记 ([BOS]) 和结束标记 ([EOS])。\n",
    "        encoder_input, attn_mask = self.src_tokenizer.encode(\n",
    "            tokens_list,\n",
    "            add_bos=True,\n",
    "            add_eos=True,\n",
    "            return_mask=True,\n",
    "            )\n",
    "        encoder_input = torch.Tensor(encoder_input).to(dtype=torch.int64)\n",
    "        \n",
    "        # 使用模型的 infer 方法对编码器输入进行推理，得到输出结果 outputs\n",
    "        outputs = model.infer(encoder_inputs=encoder_input, encoder_inputs_mask=attn_mask)\n",
    "        preds = outputs.preds.numpy() # 推理结果\n",
    "        \n",
    "        # 使用目标语言的 trg_tokenizer 对预测序列进行解码，得到解码后的目标语言句子列表 trg_decoded。\n",
    "        trg_decoded = self.trg_tokenizer.decode(preds, split=True, remove_eos=False, remove_bos=False, remove_pad=False)\n",
    "        # 使用源语言的 src_tokenizer 对编码器输入进行解码，得到解码后的源语言句子列表 src_decoded。为下面绘制热力图做准备。\n",
    "        src_decoded = self.src_tokenizer.decode(\n",
    "            encoder_input.numpy(),\n",
    "            split=True,\n",
    "            remove_bos=False,\n",
    "            remove_eos=False\n",
    "            )\n",
    "        \n",
    "         # draw the attention map of the last decoder block\n",
    "        for attn_score, cross_attn_score, src, trg in zip(\n",
    "            outputs.decoder_self_attn_scores[layer_idx], outputs.decoder_cross_attn_scores[layer_idx], src_decoded, trg_decoded):\n",
    "            if heads_list is None:# 如果没有指定heads_list，就画单个热力图\n",
    "                self.draw_attention_map(\n",
    "                    attn_score,\n",
    "                    cross_attn_score,\n",
    "                    src,\n",
    "                    trg,\n",
    "                )\n",
    "            else:# 如果指定了heads_list，就画多个热力图\n",
    "                self.draw_attention_maps(\n",
    "                    attn_score,\n",
    "                    cross_attn_score,\n",
    "                    src,\n",
    "                    trg,\n",
    "                    heads_list=heads_list,\n",
    "                    )\n",
    "        \n",
    "        #将解码后的目标语言句子列表返回，并使用 mose_detokenizer 进行去标记化，最终得到翻译后的结果。\n",
    "        return [self.mose_detokenizer.tokenize(self.pattern.sub(\"\", s).split()) for s in self.trg_tokenizer.decode(preds)] \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c5d288cf83d67b62",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T13:03:32.077356Z",
     "iopub.status.busy": "2025-02-07T13:03:32.077011Z",
     "iopub.status.idle": "2025-02-07T13:03:33.424282Z",
     "shell.execute_reply": "2025-02-07T13:03:33.423723Z",
     "shell.execute_reply.started": "2025-02-07T13:03:32.077329Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading vocabulary from ./wmt16/vocab ...\n",
      "Read 762259 words (18107 unique) from vocabulary file.\n",
      "Loading codes from ./wmt16/bpe.20000 ...\n",
      "Read 20001 codes from the codes file.\n",
      "  6%|▋         | 8/128 [00:00<00:01, 60.27it/s]\n",
      "/usr/local/lib/python3.10/site-packages/IPython/core/pylabtools.py:170: UserWarning: There are no gridspecs with layoutgrids. Possibly did not call parent GridSpec with the \"figure\" keyword\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "['man in white shirt on a lake.']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentence_list = [\n",
    "    \"Mann in einem kleinen weißen Boot auf einem See.\",  # Man in a small white boat on a lake.\n",
    "    # \"Ein Mann mit einem Eimer und ein Mädchen mit einem Hut am Strand.\", # A man with a bucket and a girl in a hat on the beach.\n",
    "    # \"Drei Männer auf Pferden während eines Rennens.\",  # Three men on horses during a race.\n",
    "    # \"Ein Mann und eine Frau essen zu Abend\",  # 一个男人和一个女人在吃晚餐\n",
    "]\n",
    "\n",
    "# load checkpoints\n",
    "model = TransformerModel(config)\n",
    "model.load_state_dict(state_dict)\n",
    "translator = Translator(model.cpu(), tokenizer, tokenizer)\n",
    "translator(\n",
    "    sentence_list,\n",
    "    layer_idx=-1,\n",
    "    # heads_list=[0, 1, 2, 3, 4, 5, 6, 7]\n",
    "    )"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
